On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey
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paper_title: Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure

paper_content:

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.
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paper_title: Adolf Beck: A Forgotten Pioneer in Electroencephalography

paper_content:

Adolf Beck, born in 1863 at Cracow (Poland), joined the Department of Physiology of the Jagiellonian University in 1880 to work directly under the supervision of the prominent physiology professor, Napoleon Cybulski. Following his suggestion, Beck started experimental studies on the electrical brain activity of animals, especially in response to sensory stimulation. Beck placed electrodes directly on the surface of brain to localize brain potentials that were evoked by sensory stimuli. He observed spontaneous fluctuations in the electrical brain activity and noted that these oscillations ceased after sensory stimulation. He published these findings concerning the electrical brain activity, such as spontaneous fluctuations, evoked potentials, and desynchronization of brain waves, in 1890 in the German language Centralblatt fur Physiologie. Moreover, an intense polemic arose between physiologists of that era on the question of who should claim being the founder of electroencephalography. Ultimately, Richard...
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paper_title: Brain waves for automatic biometric-based user recognition

paper_content:

Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimer's, Parkinson's, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.
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paper_title: Neural network based person identification using EEG features

paper_content:

A direct connection between the electroencephalogram (EEG) and the genetic information of an individual has been suspected and investigated by neurophysiologists and psychiatrists since 1960. However, most of this early as well as more recent research focuses on the classification of pathological EEG cases, aiming to construct tests for purposes of diagnosis. On the contrary, our work focuses on healthy individuals and aims to establish an one-to-one correspondence between the genetic information of the individual and certain features of his/her EEG, as an intermediate step towards the further goal of developing a test for person identification based on features extracted from the EEG. Potential applications include, among others, information encoding and decoding and access to secure information. At the present stage the proposed method uses spectral information extracted from the EEG non-parametrically via the FFT and employs a neural network (a learning vector quantizer-LVQ) to classify unknown EEGs as belonging to one of a finite number of individuals. Correct classification scores ranging from 80% to 100% in experiments conducted on real data, show evidence that the EEG indeed carries genetic information and that the proposed method can be used to construct person identification tests based on EEG features.
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paper_title: Ellen R. Grass Lecture: extraordinary EEG.

paper_content:

ABSTRACT.The electroencephalogram (EEG) is a unique measure of the electrical function of the brain. Cerebral sources of EEG potentials expressed by the human brain have been recorded on the surface of the scalp for almost 100 years. With the advances in digital technology, present day EEG has evolved to include specialized techniques that offer new capabilities never realized before. Refined intracranial recording techniques and multi-channel systems now permit access to regions of the brain that have previously been off limits to conventional EEG recording. Extraordinary biorhythms beyond 1 to 35 Hz that have been typically used in routine EEG are now exploring extreme band-widths that may disclose epileptogenic regions and their yield causative networks involved in generating seizures. These spectral frequencies ranging from infraslow activity to high frequency oscillations have far reaching implications for people with uncontrolled seizures. Micro-EEG recordings are now shedding light on electrophysio...
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paper_title: Wavelet-Based EEG Preprocessing for Biometric Applications

paper_content:

EEG signals, measuring transient brain activities, can be used as a source of biometric information with potential application in high-security person recognition scenarios. However, due to the inherent nature of these signals and the process used for their acquisition, their effective preprocessing is critical for their successful utilisation. In this paper we compare the effectiveness of different wavelet-based noise removal methods and propose an EEG-based biometric identification system which combines two such de-noising methods to enhance the signal preprocessing stage. In tests using 50 subjects from a public database, the proposed new approach is shown to provide improved identification performance over alternative techniques. Another important preprocessing consideration is the segmentation of the EEG record prior to de-noising. Different segmentation approaches were investigated and the trade-off between performance and computation time is explored. Finally the paper reports on the impact of the choice of wavelet function used for feature extraction on system performance.
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paper_title: Cerebral location of international 10-20 system electrode placement

paper_content:

Abstract We employed CT scanning to correlate scalp markers placed according to the international 10–20 system with underlying cerebral structures. Subjects were 12 normal volunteers. Measurements included assessment for cranial asymmetry to determine the effect of skull asymmetry on cortical location of electrodes. Results were correlated with the cortical histological map of Brodmann. Primary cortical locations agree well with previously published data and provide cortical localization in greater detail than previous studies. Variability of cortical electrode location was substantial in some cases and not related to cranial asymmetry. The results indicate that CT scanning or other neuroimaging techniques which reveal detailed cerebral anatomy would be potentially highly useful in defining the generators of electrocerebral potentials recorded from the scalp.
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paper_title: BCI2000: a general-purpose brain-computer interface (BCI) system

paper_content:

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.
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paper_title: Ten Percent Electrode System for Topographic Studies of Spontaneous and Evoked EEG Activities

paper_content:

ABSTRACT.An 81-electrode system is described which is designed for topographic studies of spontaneous and evoked EEG activities. This method combines the standard leads of the International 10–20 System with supplementary electrodes applied midway between leads of the 10–20 system or electrodes in turn situated between 10–20 leads. Auxiliary electrode designations refer to the underlying brain areas and to adjacent leads of the 10–20 method. The utilization of this “10% system” is suggested to promote standardization.
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paper_title: An EEG-Based Biometric System Using Eigenvector Centrality in Resting State Brain Networks

paper_content:

Recently, there has been a growing interest in the use of brain activity for biometric systems. However, so far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an approach based on phase synchronization, to investigate personal distinctive brain network organization. To this end, the importance, in terms of centrality, of different regions was determined on the basis of EEG recordings. We hypothesized that nodal centrality enables the accurate identification of individuals. EEG signals from a cohort of 109 64-channels EEGs were band-pass filtered in the classical frequency bands and functional connectivity between the sensors was estimated using the Phase Lag Index. The resulting connectivity matrix was used to construct a weighted network, from which the nodal Eigenvector Centrality was computed. Nodal centrality was successively used as feature vector. Highest recognition rates were observed in the gamma band (equal error rate ( ${\rm EER}) = 0.044$ ) and high beta band ( ${\rm EER} = 0.102$ ). Slightly lower recognition rate was observed in the low beta band ( ${\rm EER} = 0.144$ ), while poor recognition rates were observed for the others frequency bands. The reported results show that resting-state functional brain network topology provides better classification performance than using only a measure of functional connectivity, and may represent an optimal solution for the design of next generation EEG based biometric systems. This study also suggests that results from biometric systems based on high-frequency scalp EEG features should be interpreted with caution.
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paper_title: Human brain distinctiveness based on EEG spectral coherence connectivity

paper_content:

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.
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paper_title: Individual identification technique using visual evoked potential signals

paper_content:

A new individual identification technique using visual evoked potential (VEP) signals is proposed. The technique uses fuzzy ARTMAP (FA) classification of gamma band VEP response from 61 channels extracted while the individual is seeing a single picture. All ten subjects tested are classified correctly with an overall maximum classification rate of 95% across 200 test patterns. The method could be developed into a stand-alone identification system or as an addition to existing identification systems, especially for identifying a group of individuals in a company or factory.
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paper_title: Biometrics from Brain Electrical Activity: A Machine Learning Approach

paper_content:

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis
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paper_title: The mental prosthesis: Assessing the speed of a p300-based brain-computer interface

paper_content:

Describes a study designed to assess a brain-computer interface (BCI), originally described by Farwell and Donchin in 1988. The system utilizes the fact that the rare events in the oddball paradigm elicit the P300 component of the event-related potential (ERP). The BCI presents the user with a matrix of 6 by 6 cells, each containing one letter of the alphabet. The user focuses attention on the cell containing the letter to be communicated while the rows and the columns of the matrix are intensified. Each intensification is an event in the oddball sequence, the row and the column containing the attended cell are "rare" items and, therefore, only these events elicit a P300. The computer thus detects the transmitted character by determining which row and which column elicited the P300. The authors report an assessment, using a bootstrapping approach, which indicates that an off line version of the system can communicate at the rate of 7.8 characters a minute and achieve 80% accuracy. The system's performance in real time was also assessed. The authors' data indicate that a P300-based BCI is feasible and practical. However, these conclusions are based on tests using healthy individuals.
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paper_title: An Introduction to the Event-Related Potential Technique

paper_content:

The event-related potential (ERP) technique in cognitive neuroscience allows scientists to observe human brain activity that reflects specific cognitive processes. In An Introduction to the Event-Related Potential Technique, Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches. The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.
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paper_title: A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity

paper_content:

In this article we present a standardized set of 260 pictures for use in experiments investigating differences and similarities in the processing of pictures and words. The pictures are black-and-white line drawings executed according to a set of rules that provide consistency of pictorial representation. The pictures have been standardized on four variables of central relevance to memory and cognitive processing: name agreement, image agreement, familiarity, and visual complexity. The intercorrelations among the four measures were low, suggesting that the)' are indices of different attributes of the pictures. The concepts were selected to provide exemplars from several widely studied semantic categories. Sources of naming variance, and mean familiarity and complexity of the exemplars, differed significantly across the set of categories investigated. The potential significance of each of the normative variables to a number of semantic and episodic memory tasks is discussed. Investigators studying aspects of verbal processes have long had access to extensive normative data on various objective and subjective dimensions of their verbal materials. Brown (1976) recently compiled a catalog of scaled verbal materials that included 172 studies providing such information. For the set of verbal materials most comparable to the present set of pictures—English nouns—such dimensions include objective measures of frequency of occurrence and subjective measures of familiarity, age of acquisition, concreteness, imagery, meaningfulness, and emotionality. In contrast, normative data on character
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paper_title: I think, therefore i am: Usability and security of authentication using brainwaves

paper_content:

With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each subject, we can achieve 99% authentication accuracy using single-channel EEG signals, which is on par with previous research employing multi-channel EEG signals using clinical-grade devices. In addition to the usability improvement offered by the single-channel dry-contact EEG sensor, we also study the usability of different classes of mental tasks. We find that subjects have little difficulty recalling chosen “pass-thoughts” (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived difficulty and enjoyability of the tasks. These results can inform the design of authentication systems that guide users in choosing tasks that are both usable and secure.
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paper_title: Identifying Individuality Using Mental Task Based Brain Computer Interface

paper_content:

In recent years, numerous Brain Computer Interface (BCI) technologies have been developed to assist the disabled. In this paper, mental task based BCI is proposed for a different purpose: to identify the individuality of a person. The idea is based on the classification of electroencephalogram (EEG) signals recorded when a user thinks of either one or two mental tasks. As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. Sixth order autoregressive features are computed from EEG signals and classified by Linear Discriminant classifier using a modified 10 fold cross validation procedure, which gave an average error of 0.95% when tested on 400 EEG patterns from four subjects. Though the method would have to undergo further development to obtain repeatable good accuracy; this initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.
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paper_title: Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation

paper_content:

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brainwave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian mixture models and maximum a posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others
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paper_title: Cerebral location of international 10-20 system electrode placement

paper_content:

Abstract We employed CT scanning to correlate scalp markers placed according to the international 10–20 system with underlying cerebral structures. Subjects were 12 normal volunteers. Measurements included assessment for cranial asymmetry to determine the effect of skull asymmetry on cortical location of electrodes. Results were correlated with the cortical histological map of Brodmann. Primary cortical locations agree well with previously published data and provide cortical localization in greater detail than previous studies. Variability of cortical electrode location was substantial in some cases and not related to cranial asymmetry. The results indicate that CT scanning or other neuroimaging techniques which reveal detailed cerebral anatomy would be potentially highly useful in defining the generators of electrocerebral potentials recorded from the scalp.
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paper_title: Low-cost electroencephalogram (EEG) based authentication

paper_content:

A low-cost, consumer-grade, EEG-based individual authentication system is proposed in this work. While EEG signals are recorded, the subject performs four mental imagery tasks consisting of a baseline measurement, referential limb movement, counting, and rotation for 150 seconds each. The 150 seconds of data are divided into one second segments, from which features are obtained. Three sets of features are extracted from each electrode: 6th order autoregressive (AR) coefficients, power spectral density, and total power in five frequency bands. Two additional sets of features are extracted from interhemispheric data: interhemispheric power differences and interhemispheric linear complexity. These feature sets are combined into a feature vector that is then used by a linear support vector machine (SVM) with cross validation for classification. The goal was to minimize both false accept rates (FARs) and false reject rates (FRRs). Using voting rules across groups of ten segments, we were able to achieve 100% classification accuracy for each subject in each task. Though more work must be done with a larger subject pool as well as across multiple sessions, these results show that low-cost EEG authentication systems may be viable.
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paper_title: EEG biometric identification: Repeatability and influence of movement-related EEG

paper_content:

This paper describes use of EEG signal as biometric characteristic for person identification. We focus on the problem of repeatability of the identification process, and influence of the movement-related EEG on results of identification. Used database of EEG signals consists of two sessions, obtained approximately one year apart. We use Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based classifier for classification of EEG segments, which leads to subject identification with success rate for single session identification up to 98%. When the earlier session is used for classifier training and the later session for testing, the highest success rate with our identification algorithm is 87.1%. Experiments show that use of the movement-related EEG leads to better identification results.
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paper_title: I think, therefore i am: Usability and security of authentication using brainwaves

paper_content:

With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each subject, we can achieve 99% authentication accuracy using single-channel EEG signals, which is on par with previous research employing multi-channel EEG signals using clinical-grade devices. In addition to the usability improvement offered by the single-channel dry-contact EEG sensor, we also study the usability of different classes of mental tasks. We find that subjects have little difficulty recalling chosen “pass-thoughts” (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived difficulty and enjoyability of the tasks. These results can inform the design of authentication systems that guide users in choosing tasks that are both usable and secure.
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paper_title: Combination of multiple detectors for EEG based biometric identification/authentication

paper_content:

The different structures of the brain of human beings produce spontaneous electroencephalographic (EEG) records that can be used to identify subjects. This paper presents a method for biometric authorization and identification based on EEG signals. The hardware uses a simple 2-signal electrode and a reference electrode configuration. The electrodes are positioned in such a way to be as unobtrusive as possible for the tested subject. Multiple features are extracted from the EEG signals that are processed by different classifiers. The system uses all the possible combinations between classifiers and features, fusing the best results. The fused decision improves the classification performance for even a small number of observation vectors. Results were obtained from a population of 50 subjects and 20 intruders, both in authentication and identification tasks. The system obtains an Equal Error Rate (EER) of 2.4% with only a few seconds for testing. The obtained performance measures are an improvement over the results of current EEG-based systems.
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paper_title: Biometrics from Brain Electrical Activity: A Machine Learning Approach

paper_content:

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis
---
paper_title: Human brain distinctiveness based on EEG spectral coherence connectivity

paper_content:

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.
---
paper_title: Neural network based person identification using EEG features

paper_content:

A direct connection between the electroencephalogram (EEG) and the genetic information of an individual has been suspected and investigated by neurophysiologists and psychiatrists since 1960. However, most of this early as well as more recent research focuses on the classification of pathological EEG cases, aiming to construct tests for purposes of diagnosis. On the contrary, our work focuses on healthy individuals and aims to establish an one-to-one correspondence between the genetic information of the individual and certain features of his/her EEG, as an intermediate step towards the further goal of developing a test for person identification based on features extracted from the EEG. Potential applications include, among others, information encoding and decoding and access to secure information. At the present stage the proposed method uses spectral information extracted from the EEG non-parametrically via the FFT and employs a neural network (a learning vector quantizer-LVQ) to classify unknown EEGs as belonging to one of a finite number of individuals. Correct classification scores ranging from 80% to 100% in experiments conducted on real data, show evidence that the EEG indeed carries genetic information and that the proposed method can be used to construct person identification tests based on EEG features.
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paper_title: The electroencephalogram as a biometric

paper_content:

This paper examines the effectiveness electroencephalogram (EEG) as a biometric identification of individual subjects in a pool of 40 normal subjects. The EEG's second order statistics are computed using autoregressive models of various order. The coefficients in these models are then evaluated for their biometric potential. Discriminant functions applied to the model coefficients are used to examine the degree to which the subjects in the data pool can be identified. The results indicate that the EEG has significant biometric potential. In this data pool, 100% of subjects are correctly classified when all data is used, and over 80% when the functions are computed from half the data and then applied to the remaining.
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paper_title: I think, therefore i am: Usability and security of authentication using brainwaves

paper_content:

With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each subject, we can achieve 99% authentication accuracy using single-channel EEG signals, which is on par with previous research employing multi-channel EEG signals using clinical-grade devices. In addition to the usability improvement offered by the single-channel dry-contact EEG sensor, we also study the usability of different classes of mental tasks. We find that subjects have little difficulty recalling chosen “pass-thoughts” (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived difficulty and enjoyability of the tasks. These results can inform the design of authentication systems that guide users in choosing tasks that are both usable and secure.
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paper_title: A new look at the statistical model identification

paper_content:

The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.
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paper_title: EEG signal preprocessing for biometric recognition

paper_content:

Electroencephalography (EEG) has been recently investigated as a biometric modality for automatic people recognition purposes. Several studies have shown that brain signals possess subject-specific traits that allow distinguishing people. Nonetheless, extracting discriminative characteristics from EEG recordings may be a challenging task, due to the significant presence of artifacts in the acquired data. In order to cope with such issue, in this paper we evaluate the effectiveness of some preprocessing techniques in automatically removing undesired EEG contributions, with the aim of improving the achievable recognition rates. Specifically, methods based on blind source separation and sample entropy estimation are here investigated. An extensive set of experimental tests, performed over a large database comprising recordings taken from 50 healthy subjects during three distinct sessions spanning a period of about one month, in both eyes-closed and eyes-open conditions, is carried out to analyze the performance of the proposed approaches.
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paper_title: The mental prosthesis: Assessing the speed of a p300-based brain-computer interface

paper_content:

Describes a study designed to assess a brain-computer interface (BCI), originally described by Farwell and Donchin in 1988. The system utilizes the fact that the rare events in the oddball paradigm elicit the P300 component of the event-related potential (ERP). The BCI presents the user with a matrix of 6 by 6 cells, each containing one letter of the alphabet. The user focuses attention on the cell containing the letter to be communicated while the rows and the columns of the matrix are intensified. Each intensification is an event in the oddball sequence, the row and the column containing the attended cell are "rare" items and, therefore, only these events elicit a P300. The computer thus detects the transmitted character by determining which row and which column elicited the P300. The authors report an assessment, using a bootstrapping approach, which indicates that an off line version of the system can communicate at the rate of 7.8 characters a minute and achieve 80% accuracy. The system's performance in real time was also assessed. The authors' data indicate that a P300-based BCI is feasible and practical. However, these conclusions are based on tests using healthy individuals.
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paper_title: Brain waves based user recognition using the “eyes closed resting conditions” protocol

paper_content:

In this paper the use of brain waves as a biometric identifier is investigated. Among the very different protocols that can be used to acquire the electroencephalogram signal (EEG) of an individual we rely on a very simple one: closed eyes in resting conditions. A database of 48 healthy subjects, collected by the authors at the neurophysiology laboratory of the IRCCS Fondazione Santa Lucia, Roma, Italy, has been used for the experiments. Signals acquired from triplets of electrodes have been employed in the experimentations. In more detail, ten different triplets have been used separately in the experiments in order to speculate about the most suitable triplet to capture the occurring phenomena. Feature vectors constituted by the reflection coefficients of a six order AR model have been extracted for each used channel thus giving rise to a feature vector of length eighteen. A polynomial regression based classification is then employed. This analysis has been performed for three different frequency bands for each of the ten different triplet under analysis. The obtained genuine acceptance rate is of 96.08%.
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paper_title: An Identification System Based on Portable EEG Acquisition Equipment

paper_content:

EEG-based identification system can effectively improve the security in the existing biometric systems because of its uniqueness, stealthiness, non-imitability and receipt-freeness. However, owing to the drawbacks in data acquisition equipments and signal feature extraction methods, the development of EEG-based identification system remains in the experimental stage. In this paper, portable acquisition equipment was used for data collection. After evaluating the signals, the AR model parameters were extracted from the valid datum as the identification features. Combined with these features, an identification system was realized based on three pattern recognition methods including BP neural network, support vector machines(SVM) and linear discriminant analysis(LDA). The conclusions indicate that: 1. The data quality can be effectively improved when the subject is fully relaxed and keep his/her eyes closed during data collection. 2. The AR model parameters of EEG signals can be used for identification. 3. The efficiency of SVM is the highest and the identification rate is up to 87%.
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paper_title: Parametric person identification from the EEG using computational geometry

paper_content:

Person identification based on features extracted parametrically from the EEG spectrum is investigated in this work. The method proposed utilizes computational geometry algorithms (convex polygon intersections), appropriately modified, in order to classify unknown EEGs. The signal processing step includes EEG spectral analysis for feature extraction, by fitting a linear model of the AR type on the alpha rhythm EEG signal. The correct classification scores obtained on real EEG data experiments (91% in the worst case) are promising in that they corroborate existing evidence that EEG carries genetically specific information and is therefore appropriate as a basis for person identification methods.
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paper_title: Wavelet-Based EEG Preprocessing for Biometric Applications

paper_content:

EEG signals, measuring transient brain activities, can be used as a source of biometric information with potential application in high-security person recognition scenarios. However, due to the inherent nature of these signals and the process used for their acquisition, their effective preprocessing is critical for their successful utilisation. In this paper we compare the effectiveness of different wavelet-based noise removal methods and propose an EEG-based biometric identification system which combines two such de-noising methods to enhance the signal preprocessing stage. In tests using 50 subjects from a public database, the proposed new approach is shown to provide improved identification performance over alternative techniques. Another important preprocessing consideration is the segmentation of the EEG record prior to de-noising. Different segmentation approaches were investigated and the trade-off between performance and computation time is explored. Finally the paper reports on the impact of the choice of wavelet function used for feature extraction on system performance.
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paper_title: Quality filtering of EEG signals for enhanced biometric recognition

paper_content:

In this paper we present a biometric person recognition system based on EEG signals incorporating a novel strategy to find and utilize the most informative data segments using the concept of Sample Entropy. The users are presented with a stimulus that prompts a motor-imagery response. This is then measured using an array of EEG sensors. A sliding-window segmentation scheme and Wavelet Packet Decomposition are adopted for primary feature extraction before the quality measurement stage. The quality-filtered feature windows are then used to extract secondary features that are in turn classified using a linear discriminant classifier. The proposed system is tested using a publicly available EEG database and it shows that entropy filtering results in a significant improvement on performance. An average identification accuracy rate of more than 90% is achieved for 109 subjects using only eight electrodes, utilizing only the highest quality for each subject.
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paper_title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

paper_content:

A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...
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paper_title: Empirical mode decomposition as a filter bank

paper_content:

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions. It is also pointed out that the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.
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paper_title: ON INSTANTANEOUS FREQUENCY

paper_content:

Instantaneous frequency (IF) is necessary for understanding the detailed mechanisms for nonlinear and nonstationary processes. Historically, IF was computed from analytic signal (AS) through the Hilbert transform. This paper offers an overview of the difficulties involved in using AS, and two new methods to overcome the difficulties for computing IF. The first approach is to compute the quadrature (defined here as a simple 90° shift of phase angle) directly. The second approach is designated as the normalized Hilbert transform (NHT), which consists of applying the Hilbert transform to the empirically determined FM signals. Additionally, we have also introduced alternative methods to compute local frequency, the generalized zero-crossing (GZC), and the teager energy operator (TEO) methods. Through careful comparisons, we found that the NHT and direct quadrature gave the best overall performance. While the TEO method is the most localized, it is limited to data from linear processes, the GZC method is the m...
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paper_title: Novel HHT-Based Features for Biometric Identification Using EEG Signals

paper_content:

In this paper we present a novel approach for biometric identification using electroencephalogram (EEG) signals based on features extracted with the Hilbert-Huang Transform (HHT). The instantaneous amplitude and the instantaneous frequency were computed after the HHT, and these were then used to generate the features for classification. The proposed system was evaluated using two publicly available databases in scenarios where only a single electrode is used to provide biometric information. One database (with 122 subjects) has the users viewing a series of pictures while the other one (with 109 subjects) has the users performing motor/imagery tasks. Average identification accuracies of 96% and 99% were reached for these two databases respectively using only a single electrode. These compare favourably with previously published results employing a variety of other features and classification approaches.
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paper_title: Feature extraction using emprical mode decomposition for biometric system

paper_content:

Biometric system is one of the most popular tool for user identification where security is primary concern such as Banks, airports and companies etc. Few of biometrics tool such as fingureprints, face and Iris are frequently using for user identification, nevertheless its flaw like mimic the original data makes it weak. Hence, there is another biometric has been emerged based on EEG where mimicry of data is quite impossible because it depends on users thought. The following article presents a new and unique template using empirical mode decomposition as a feature for classification each from another one. In order to do this work we have taken four regions of the brain such as frontal, cerebral, parietal, and occipital and we have observed that each and every individual has unique pattern.
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paper_title: Empirical mode decomposition as a filter bank

paper_content:

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions. It is also pointed out that the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.
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paper_title: Eeg-based person authentication using face stimuli

paper_content:

In this paper, we devise a novel stimulus presentation paradigm, using self-face and non-self-face images as stimuli for a person authentication system based on the studies in psychology and neurophysiology. Based on our experimental results and analysis, it is believed that the proposed person authentication system can be considered as a new biometric authentication system due to its universality, distinctiveness, permanence, collectability and uniqueness. 1
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paper_title: Single-Trial Analysis and Classification of ERP Components - a Tutorial

paper_content:

Abstract Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference pattern of ERPs and a spatial filter which cancels non task-related brain activity.
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paper_title: Person Identification Using Evoked Potentials and Peak Matching

paper_content:

In this paper, we explore visually evoked potentials (VEPs) as a potential tool for biometric identification. Using a clinical stimulation paradigm, single channel pattern onset VEPs are recorded from raw EEG from 10 healthy male subjects aged between 20 and 24. Following this, two feature extraction techniques are employed to characterize the signals. Specifically, a novel, physiologically relevant peak matching algorithm is proposed and its performance is compared to features obtained from multi-resolution wavelet analysis. Once suitably characterized, the VEPs from different individuals are classified using a standard distance-measure based algorithm.
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paper_title: Human identification with electroencephalogram (EEG) signal processing

paper_content:

Human identification becomes huge demand in particular for the security related areas. Biometric systems can employ different kinds of features, e.g., features of fingerprint, face, iris or posture. EEG signals are the signature of neural activities. It is confidential and hard to imitate, since EEG signals are a reflection of individual-dependent inner mental tasks. It has several advantages, such as (i) it is confidential as it corresponds to a mental task, (ii) it is very difficult to mimic and (iii) it is almost impossible to steal as the brain activity is sensitive to the stress and the mood of the person, an aggressor cannot force the person to reproduce his/her mental pass-phrase. In this paper we first proposed a novel algorithm to create a spatial pattern of EEG signals obtained from the open public database. In our EEG signal processing, we have analyzed 64-electrode EEG samples for two databases, one is for 45 people and calculate the equivalent root mean square (rms) values for each electrode signal over 1 second period, by which created a 64-value input for each subject. With neural network (NN) model, our analysis showed that our designed classifier is able to identify all the 45 people correctly (successful rate of 100%) with a mean square error of 2.0334×10−7 and the same algorithm applying to the 2nd database with 116 out of 122 people can be fully identified (successful rate of 95.1%) with a mean square error value of 0.00186. We deeply believe that a low complexity, high resolution, effective and efficient is very attractive for the real life applications become true in the foreseeable future
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paper_title: Using Shannon Entropy as EEG Signal Feature for Fast Person Identification

paper_content:

Identication accuracy and speed are important factors in automatic person identication systems. In this paper, we propose a feature extraction method to extract brain wave features from dierent brain rhythms of electroencephalography (EEG) signal for the purpose of fast, yet accurate person identication. The proposed feature extraction method is based on the fact that EEG signal is complex, non-stationary, and non-linear. With this fact, non-linear analysis like entropy would be more appropriate. Shannon entropy (SE) based EEG features from al- pha, beta, and gamma wave bands are extracted and evaluated for person identication. Experimental results show that SE features provide high person identication rates yet with a low feature dimension, thus better performance.
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paper_title: Evaluation of recording factors in EEG-based personal identification: A vital step in real implementations

paper_content:

Although the concept of using brain waves, e.g. Electroencephalogram (EEG) in personal identification has been validated, some unanswered practical and theoretical questions prevent this technology from further development for commercialization. In this paper, the recording factors (e.g. diet, times of day, etc.) affecting a personal EEG recordings are investigated. Experimental results show that recording factors have some effects in the performance of EEG-based personal identification system. This research suggests that it is necessary to build more comprehensive training sets to obtain a higher system performance. Based on a well-designed personal identification EEG recording experiment, it is the first time to give the quantitative measurements of the diet and person's circadian effects on the performance of EEG-based personal identification system.
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paper_title: Fisher linear discriminant based person identification using visual evoked potentials

paper_content:

Biometrics is the technique of uniquely recognizing a person among a group of people. It is usually performed based on one or more of humanpsilas intrinsic physical or behavioral traits. One such trait is the electroencephalogram (EEG) signal. In this paper, the feasibility of visual evoked potential (VEP) in the gamma band of EEG signal, as a physiological trait, is studied, and used to identify individuals in a group of 20 people. To this end, the parameters of the AR model together with the peak of the power spectrum density (PSD) of the gamma band VEP signal (GMVEP) are considered as main useful features. Next, the Fisherpsilas linear discriminant (FLD) is used to reduce the feature vector dimensions. Finally, the k nearest neighborhood (KNN) technique is employed to classify the data and the leave-one-out cross validation method is used for accuracy assessment. A correct classification rate of 100% is achieved.
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paper_title: Machine learning: a review of classification and combining techniques

paper_content:

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy--ensembles of classifiers.
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paper_title: Melting Point Prediction Employing k-Nearest Neighbor Algorithms and Genetic Parameter Optimization

paper_content:

We have applied the k-nearest neighbor (kNN) modeling technique to the prediction of melting points. A data set of 4119 diverse organic molecules (data set 1) and an additional set of 277 drugs (data set 2) were used to compare performance in different regions of chemical space, and we investigated the influence of the number of nearest neighbors using different types of molecular descriptors. To compute the prediction on the basis of the melting temperatures of the nearest neighbors, we used four different methods (arithmetic and geometric average, inverse distance weighting, and exponential weighting), of which the exponential weighting scheme yielded the best results. We assessed our model via a 25-fold Monte Carlo cross-validation (with approximately 30% of the total data as a test set) and optimized it using a genetic algorithm. Predictions for drugs based on drugs (separate training and test sets each taken from data set 2) were found to be considerably better [root-mean-squared error (RMSE)=46.3 degrees C, r2=0.30] than those based on nondrugs (prediction of data set 2 based on the training set from data set 1, RMSE=50.3 degrees C, r2=0.20). The optimized model yields an average RMSE as low as 46.2 degrees C (r2=0.49) for data set 1, and an average RMSE of 42.2 degrees C (r2=0.42) for data set 2. It is shown that the kNN method inherently introduces a systematic error in melting point prediction. Much of the remaining error can be attributed to the lack of information about interactions in the liquid state, which are not well-captured by molecular descriptors.
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paper_title: Electroencephalogram signals from imagined activities : A novel biometric identifier for a small population

paper_content:

Electroencephalogram (EEG) signals extracted during imagined activities have been studied for use in Brain Computer Interface (BCI) applications. The major hurdle in the EEG based BCI is that the EEG signals are unique to each individual. This complicates a universal BCI design. On the contrary, this disadvantage is the advantage when it comes to using EEG signals from imagined activities for biometric applications. Therefore, in this paper, EEG signals from imagined activities are proposed as a biometric to identify the individuality of persons. The approach is based on the classification of EEG signals recorded when a user performs either one or several mental activities (up to five). As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. A total of 108 features (autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences and inter-hemispheric channel linear complexity values) are computed from each EEG segment for each mental activity and classified by a linear discriminant classifier using a modified 10 fold cross validation procedure, which gave perfect classification when tested on 500 EEG patterns from five subjects. This initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.
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paper_title: EEG biometric identification: Repeatability and influence of movement-related EEG

paper_content:

This paper describes use of EEG signal as biometric characteristic for person identification. We focus on the problem of repeatability of the identification process, and influence of the movement-related EEG on results of identification. Used database of EEG signals consists of two sessions, obtained approximately one year apart. We use Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based classifier for classification of EEG segments, which leads to subject identification with success rate for single session identification up to 98%. When the earlier session is used for classifier training and the later session for testing, the highest success rate with our identification algorithm is 87.1%. Experiments show that use of the movement-related EEG leads to better identification results.
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paper_title: Multi-column Deep Neural Networks for Image Classification

paper_content:

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.
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paper_title: PIN Generation Using Single Channel EEG Biometric

paper_content:

This paper investigates a method to generate personal identification number (PIN) using brain activity recorded from a single active electroencephalogram (EEG) channel. EEG based biometric to generate PIN is less prone to fraud and the method is based on the recent developments in brain-computer interface (BCI) technology, specifically P300 based BCI designs. Our perfect classification accuracies from three subjects indicate promise for generating PIN using thought activity measured from a single channel.
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paper_title: A Novel Connectionist System for Unconstrained Handwriting Recognition

paper_content:

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.
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paper_title: Exploring EEG-based biometrics for user identification and authentication

paper_content:

As human brain activities, represented by EEG brainwave signals, are more confidential, sensitive, and hard to steal and replicate, they hold great promise to provide a far more secure biometric approach for user identification and authentication. In this study, we present an EEG-based biometric security framework. Specifically, we propose to reduce the noise level through ensemble averaging and low-pass filter, extract frequency features using wavelet packet decomposition, and perform classification based on an artificial neural network. We explicitly discuss four different scenarios to emulate different application cases in authentication. Experimental results show that: the classification rates of distinguishing one subject or a small group of individuals (e.g., authorized personnel) from others (e.g., unauthorized personnel) can reach around 90%. However, it is also shown that recognizing each individual subject from a large pool has the worst performance with a classification rate of less than 11%. The side-by-side method shows an improvement on identifying all the subjects with classification rates of around 40%. Our study lays a solid foundation for future investigation of innovative, brainwave-based biometric approaches.
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paper_title: Machine learning: a review of classification and combining techniques

paper_content:

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy--ensembles of classifiers.
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paper_title: Method of identifying individuals using VEP signals and neural network

paper_content:

A method of identifying individuals using visual-evoked-potential (VEP) signals and neural network (NN) is proposed. In the approach,a backpropagation (BP) NN is trained to identify individuals using gamma-band (30-50 Hz) spectral power ratio of VEP signals extracted from 61 electrodes located on the scalp of the brain. The gamma-band spectral-power ratio is computed using a zero-phase Butterworth digital filter and Parseval's time-frequency equivalence theorem. NN classification gives an average of 99.06% across 400 test VEP patterns from 20 individuals using 10-fold cross-validation scheme. This shows promise for the approach to be developed further as a biometric identification system.
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paper_title: Neural network based person identification using EEG features

paper_content:

A direct connection between the electroencephalogram (EEG) and the genetic information of an individual has been suspected and investigated by neurophysiologists and psychiatrists since 1960. However, most of this early as well as more recent research focuses on the classification of pathological EEG cases, aiming to construct tests for purposes of diagnosis. On the contrary, our work focuses on healthy individuals and aims to establish an one-to-one correspondence between the genetic information of the individual and certain features of his/her EEG, as an intermediate step towards the further goal of developing a test for person identification based on features extracted from the EEG. Potential applications include, among others, information encoding and decoding and access to secure information. At the present stage the proposed method uses spectral information extracted from the EEG non-parametrically via the FFT and employs a neural network (a learning vector quantizer-LVQ) to classify unknown EEGs as belonging to one of a finite number of individuals. Correct classification scores ranging from 80% to 100% in experiments conducted on real data, show evidence that the EEG indeed carries genetic information and that the proposed method can be used to construct person identification tests based on EEG features.
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paper_title: Multi-column Deep Neural Networks for Image Classification

paper_content:

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.
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paper_title: Support-vector networks

paper_content:

Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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paper_title: Comparison of Different Classifiers for Biometric System Based on EEG Signals

paper_content:

Characteristics in EEG signals related to the motor imagery can be used to build up a biometric system. However, for the practical implementation of a biometric system, the classifier plays a crucial role. In this paper, I compared the performance of three different classifiers for the detection of the imagined movements in a group of subjects on the basis of EEG signals. The classifiers compared here were those based on Linear Discrimination Analysis (LDA), Artificial Neural Network (ANN) and Support Virtual Machine (SVM). Results show a better performance of the LDA classifier with the respect to the other classifiers.
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paper_title: Low-cost electroencephalogram (EEG) based authentication

paper_content:

A low-cost, consumer-grade, EEG-based individual authentication system is proposed in this work. While EEG signals are recorded, the subject performs four mental imagery tasks consisting of a baseline measurement, referential limb movement, counting, and rotation for 150 seconds each. The 150 seconds of data are divided into one second segments, from which features are obtained. Three sets of features are extracted from each electrode: 6th order autoregressive (AR) coefficients, power spectral density, and total power in five frequency bands. Two additional sets of features are extracted from interhemispheric data: interhemispheric power differences and interhemispheric linear complexity. These feature sets are combined into a feature vector that is then used by a linear support vector machine (SVM) with cross validation for classification. The goal was to minimize both false accept rates (FARs) and false reject rates (FRRs). Using voting rules across groups of ten segments, we were able to achieve 100% classification accuracy for each subject in each task. Though more work must be done with a larger subject pool as well as across multiple sessions, these results show that low-cost EEG authentication systems may be viable.
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paper_title: A Comparison of Methods for Multiclass Support Vector Machines

paper_content:

Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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paper_title: Improving visual evoked potential feature classification for person recognition using PCA and normalization

paper_content:

In earlier papers, it was shown that recognizing persons using their brain patterns evoked during visual stimulus is possible. In this paper, several modifications are proposed to improve the recognition accuracy. In the method, gamma band spectral power (GBSP) features were computed from the visual evoked potential (VEP) signals recorded from 61 electrodes while subjects perceived a picture. Two methods were used to improve the classification rate. First, principal component analysis (PCA) was used to reduce the noise and background electroencephalogram (EEG) effects from the VEP signals. Second, the GBSP of each channel was normalized by the total GBSP from all the channels. Three classifiers were used: simplified fuzzy ARTMAP (SFA), linear discriminant (LD) and k-nearest neighbor (kNN). The experimental results using 800 VEP signals from 20 subjects with leave-one-out cross-validation strategy showed that PCA improves the classification performance for all the classifiers with normalization giving improved results in certain cases. The best classification performance of 96.50% obtained using the improved method shows that brain signals have suitable biometric properties that could be further exploited.
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paper_title: The electroencephalogram as a biometric

paper_content:

This paper examines the effectiveness electroencephalogram (EEG) as a biometric identification of individual subjects in a pool of 40 normal subjects. The EEG's second order statistics are computed using autoregressive models of various order. The coefficients in these models are then evaluated for their biometric potential. Discriminant functions applied to the model coefficients are used to examine the degree to which the subjects in the data pool can be identified. The results indicate that the EEG has significant biometric potential. In this data pool, 100% of subjects are correctly classified when all data is used, and over 80% when the functions are computed from half the data and then applied to the remaining.
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Title: On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Section 1: INTRODUCTION
Description 1: This section introduces the concept of electroencephalography (EEG), its historical background, and its potential use for biometric recognition. It also outlines the structure and goals of the paper.

Section 2: SIGNAL ACQUISITION
Description 2: This section discusses different types of sensor systems used for EEG data acquisition, the importance of the brain’s state during data acquisition, and brain stimulation methods. It also covers the challenges involved with EEG data collection for biometric purposes.

Section 3: FEATURE EXTRACTION
Description 3: This section reviews various methods of feature extraction from EEG signals including Fourier Methods (PSD), Autoregressive Models (AR), Wavelet Transform (WT), Hilbert-Huang Transform (HHT), and other novel methods. It highlights the rationale behind each technique and summarizes their application in the literature.

Section 4: FEATURE CLASSIFICATION
Description 4: This section explores different classification techniques used for EEG biometric systems such as k-Nearest Neighbor (k-NN) algorithms, Linear Discriminate Analysis (LDA), Artificial Neural Networks (ANNs), and Kernel Methods. The performance and characteristics of each technique are reviewed.

Section 5: USABILITY INVESTIGATION OF THE REPORTED RESULTS
Description 5: This section proposes a novel usability metric to evaluate EEG-based biometric systems based on multiple factors including recognition accuracy, number of subjects, number of electrodes, and recording duration. It also provides comparisons of usability scores for systems employing different feature extraction methods and discusses overall trends.

Section 6: CONCLUSION AND FUTURE WORK
Description 6: This section summarizes the findings of the survey, addressing the progress and challenges in EEG biometric recognition. It also outlines the future directions for improving the usability and effectiveness of EEG-based biometric systems.