A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals
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paper_title: Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging ( fMRI ) : methodology and exemplary data

paper_content:

Abstract A brain–computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral–ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.
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paper_title: Event-related EEG/MEG synchronization and desynchronization: basic principles

paper_content:

Abstract An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing EEG/MEG in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The most important difference between both phenomena is that the ERD/ERS is highly frequency band-specific, whereby either the same or different locations on the scalp can display ERD and ERS simultaneously. Quantification of ERD/ERS in time and space is demonstrated on data from a number of movement experiments.
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paper_title: Single-trial classification of ERPS using second-order blind identification (SOBI)

paper_content:

Single-trial classification of EEG signals has received increasing attention in both basic research and for the development of EEG based brain computer interfaces (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Since EEG sensor signals are mixtures of signals from multiple intra- and extra-cranial sources, single-trial sensory and motor evoked potentials can be difficult to detect and classify. In this paper, second-order blind identification (SOBI) was used to preprocess EEG data and extract activity from the left and right primary somatosensory (SI) cortices. Subsequently, classification of event-related potentials (ERPs) evoked by a sequence of randomly mixed left, right, and bilateral median nerve stimulations was performed by back-propagation neural networks, using as inputs the two SOBI-recovered SI components or the two "best sensors". Results from four subjects showed that classification accuracy was significantly higher when SOBI-recovered left and right SI components were used for classification than when the EEG sensor signals were used directly.
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paper_title: The effects of self-movement, observation, and imagination on /spl mu/ rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI)

paper_content:

Current movement-based brain-computer interfaces (BCI's) utilize spontaneous electroencephalogram (EEG) rhythms associated with movement, such as the /spl mu/ rhythm, or responses time-locked to movements that are averaged across multiple trials, such as the readiness potential (RP), as control signals. In one study, the authors report that the /spl mu/ rhythm is not only modulated by the expression of self-generated movement but also by the observation and imagination of movement. In another study, the authors show that simultaneous self-generated multiple limb movements exhibit properties distinct from those of single limb movements. Identification and classification of these signals with pattern recognition techniques provides the basis for the development of a practical BCI.
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paper_title: Closed-loop cortical control of direction using support vector machines

paper_content:

Motor neuroprosthetics research has focused on reproducing natural limb motions by correlating firing rates of cortical neurons to continuous movement parameters. We propose an alternative system where specific spatial-temporal spike patterns, emerging in tasks, allow detection of classes of behavior with the aid of sophisticated nonlinear classification algorithms. Specifically, we attempt to examine ensemble activity from motor cortical neurons, not to reproduce the action this neural activity normally precedes, but rather to predict an output supervisory command to potentially control a vehicle. To demonstrate the principle, this design approach was implemented in a discrete directional task taking a small number of motor cortical signals (8-10 single units) fed into a support vector machine (SVM) to produce the commands Left and Right. In this study, rats were placed in a conditioning chamber performing a binary paddle pressing task mimicking the control of a wheelchair turning left or right. Four animal subjects (male Sprague-Dawley rats) were able to use such a brain-machine interface (BMI) with an average accuracy of 78% on their first day of exposure. Additionally, one animal continued to use the interface for three consecutive days with an average accuracy over 90%.
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paper_title: On-line EEG classification during externally-paced hand movements using a neural network-based classifier

paper_content:

Abstract EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: ‘Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?’ For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89–100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people.
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paper_title: Motor imagery classification by means of source analysis methods

paper_content:

We report our investigation of classification of imagined left and right hand movements by applying source analysis methods. Independent component analysis (ICA) is used as a spatio-temporal filter, then equivalent dipole analysis and cortical current density imaging methods are applied to reconstruct equivalent sources, to aid classification of motor imagery tasks in a human subject. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis.
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paper_title: Imagery of motor actions: Differential effects of kinesthetic and visual–motor mode of imagery in single-trial EEG

paper_content:

Abstract Single-trial motor imagery classification is an integral part of a number of brain–computer interface (BCI) systems. The possible significance of the kind of imagery, involving rather kinesthetic or visual representations of actions, was addressed using the following experimental conditions: kinesthetic motor imagery (MIK), visual–motor imagery (MIV), motor execution (ME) and observation of movement (OOM). Based on multi-channel EEG recordings in 14 right-handed participants, we applied a learning classifier, the distinction sensitive learning vector quantization (DSLVQ) to identify relevant features (i.e., frequency bands, electrode sites) for recognition of the respective mental states. For ME and OOM, the overall classification accuracies were about 80%. The rates obtained for MIK (67%) were better than the results of MIV (56%). Moreover, the focus of activity during kinesthetic imagery was found close to the sensorimotor hand area, whereas visual–motor imagery did not reveal a clear spatial pattern. Consequently, to improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.
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paper_title: Automatic differentiation of multichannel EEG signals

paper_content:

Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. The authors present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.
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paper_title: Motor imagery and direct brain-computer communication

paper_content:

Motor imagery can modify the neuronal activity in the primary sensorimotor areas in a very similar way as observable with a real executed movement. One part of EEG-based brain-computer interfaces (BCI) is based on the recording and classification of circumscribed and transient EEG changes during different types of motor imagery such as, e.g., imagination of left-hand, right-hand, or foot movement. Features such as, e.g., band power or adaptive autoregressive parameters are either extracted in bipolar EEG recordings overlaying sensorimotor areas or from an array of electrodes located over central and neighboring areas. For the classification of the features, linear discrimination analysis and neural networks are used. Characteristic for the Graz BCI is that a classifier is set up in a learning session and updated after one or more sessions with online feedback using the procedure of "rapid prototyping." As a result, a discrimination of two brain states (e.g., leftversus right-hand movement imagination) can be reached within only a few days of training. At this time, a tetraplegic patient is able to operate an EEG-based control of a hand orthosis with nearly 100% classification accuracy by mental imagination of specific motor commands.
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paper_title: EEG-based communication and control: short-term role of feedback

paper_content:

When people learn to control the amplitudes of certain electroencephalogram (EEG) components (e.g., the 8-12 Hz /spl mu/-rhythm over sensorimotor cortex) and use them to move a cursor to a target on a video screen, feedback about performance is normally provided by cursor movement and by trial outcome (i.e., success or failure). The authors assessed the short-term effects of this feedback on EEG control. After subjects received initial training with feedback present, feedback was removed intermittently for periods of several minutes. Subjects still displayed EEG control when feedback was removed. Removal of cursor movement alone appeared to have effects comparable to removal of both cursor movement and trial outcome. These results show that, in the short-term at least, /spl mu/-rhythm control is not dependent on the sensory input provided by cursor movement. They also suggest that feedback can have inhibitory as well as facilitatory effects on EEG control, and that these effects vary across subjects. This finding has implications for the design of training procedures.
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paper_title: Timing of EEG-based cursor control.

paper_content:

In a gas insulated high voltage cable having an outer tubular, corrugated conductor and an inner centrally disposed conductor, a spacer construction for holding and spacing the inner conductor in the tubular outer conductor, wherein each spacer has at least two spacer elements of semi annular construction each and complementing each other for establishing a closed annulus, each element constructed for laterial slipping onto the inner conductor, and the two spacer elements are secured to each other to establish the closed annulus in positive disposition on the inner conductor. Additional features impede local voltage breakdown, positive interlock of parts and resilient load relief.
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paper_title: Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project

paper_content:

In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair).
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paper_title: "Virtual keyboard" controlled by spontaneous EEG activity

paper_content:

A "virtual keyboard" (VK) is a letter spelling device operated for example by spontaneous electroencephalogram (EEG), whereby the EEC is modulated by mental hand and leg motor imagery. We report on three able-bodied subjects, operating the VK. The ability in the use of the VK varies between 0.85 and 0.5 letters/min in error-free writing.
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paper_title: Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface

paper_content:

This paper describes a simple but very powerful method for feature selection. The Distinction Sensitive Learning Vector Quantizer (DSLVQ) is a learning classifier which focuses on relevant features according to its own instance based classifications. Two different experiments describe the application of DSLVQ as a feature selector for an EEG-based Brain Computer Interface (BCI) system. It is shown that optimal electrode positions as well as frequency bands are strongly dependent on each subject and that a subject specific feature selection is when important for BCI systems.
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paper_title: Graz-BCI: state of the art and clinical applications

paper_content:

The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity.
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paper_title: Using adaptive autoregressive parameters for a brain-computer-interface experiment

paper_content:

In an online EEG discrimination task, continuous feedback was presented. The EEG was recorded during imagination of left and right hand movement and analyzed with adaptive autoregressive parameters. The parameter discrimination was fed back in form of a rectangular bar on a computer screen over a period of four seconds. An online classification result of more than 90% was obtained after a few sessions.
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paper_title: FEATURE EXTRACTION IN DEVELOPMENT OF BRAIN-COMPUTER INTERFACE: A CASE STUDY

paper_content:

In this study we compared two spectral estimation methods for feature extraction in development of computer access for communication and control based on electroencephalographic (EEG) signals. We compared power spectrum calculated by fast Fourier transform (FFT) and auto regressive (AR) coefficients calculated by Burg's algorithm. We analyzed four surface EEG signals recorded above sensory- motor areas, while the subject was attempting to use only mental activities to modulate his EEG signals resulting in desired simple movements of animated object on the feedback computer screen. To establish communication channel based on EEG signals, we asked our subject to determine which mental activity produced reproducible control actions. During the on- line training we used AR coefficients of recorded EEG signals for the feedback generating variables, i.e. the object's movement direction and speed were dependent on the EEG pattern. Our subject was able to determine which mental activity resulted in desired movements and to learn to control object movements with more than 90% accuracy. We performed off-line analysis to determine if FFT or AR can be more successful in extracting relevant information from the EEG signals before performing pattern recognition by a machine learning classifier. Probability of misclassification was used as the outcome measure. Off-line analysis showed that the overall best classification accuracy achieved with AR exceeds that achieved with FFT.
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paper_title: Noninvasive brain-actuated control of a mobile robot by human EEG

paper_content:

Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74.
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paper_title: Implementation of a telemonitoring system for the control of an EEG-based brain-computer interface

paper_content:

By the use of a brain-computer interface (BCI), it is possible for completely paralyzed patients, who have lost their ability to speak, to have a new possibility to communicate with their environment. The training with such a BCI system can be performed at the patient's home, if there is a responsible person present who is familiar with the system. This person has to adjust different parameters and to adapt the training individually to each patient. Since this function is usually taken over by the developers of the system, the number of patients who can be included in regular BCI training is restricted due to geographical distances. This paper describes the implementation of a telemonitoring system, which makes it possible for the developer to control and supervise the BCI training from his or her own place of work. First experiences with a patient living far away from the developer's lab are reported.
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paper_title: Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

paper_content:

In this paper, we have designed a two-state brain–computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.
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paper_title: EEG-based neuroprosthesis control: A step towards clinical practice

paper_content:

Abstract This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain–Computer Interface (BCI) with an implanted neuroprosthesis (Freehand ® system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand ® system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain–Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.
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paper_title: Relevant EEG features for the classification of spontaneous motor-related tasks

paper_content:

There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e., imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motor-related mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular, it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus, the use of just those relevant features increases the efficiency of the brain interfaces and, most importantly, enables a greater level of adaptation of the personal BCI to the individual user.
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paper_title: SUBJECT SPECIFIC EEG PATTERNS DURING MOTOR IMAGINARY

paper_content:

2 ABSTRACT: Left and right hand movement imagery is accompanied by an EEG event-related desynchronization (ERD) over the contralateral hand area. The time courses of the ERD from two brain-computer interface experiments were investigated by the calculation of instantaneous band power changes and by adaptive autoregressive model parameters combined with linear discriminant analysis. Subject specific differences of the EEG reactivity patterns were observed.
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paper_title: ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia

paper_content:

The aim of the present study was to demonstrate the first time the non-invasive restoration of hand grasp function in a tetraplegic patient by electroencephalogram (EEG)-recording and functional electrical stimulation (FES) using surface electrodes. The patient was able to generate bursts of beta oscillations in the EEG by imagination of foot movement. These beta bursts were analyzed and classified by a brain-computer interface (BCI) and the output signal used to control a FES device. The patient was able to grasp a cylinder with the paralyzed hand.
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paper_title: EEG-based communication: presence of an error potential

paper_content:

Abstract Background : EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect. Methods : In an effort to develop a method for detecting errors, this study compared the EEG immediately after correct target selection to that after incorrect selection. Results : The data showed that a mistake is followed by a positive potential centered at the vertex that peaks about 180 ms after the incorrect selection. Conclusion : The results suggest that this error potential might provide a method for detecting and voiding errors that requires no additional time and could thereby improve the speed and accuracy of EEG-based communication.
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paper_title: Automatic differentiation of multichannel EEG signals

paper_content:

Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. The authors present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.
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paper_title: Frequency component selection for an EEG-based brain to computer interface

paper_content:

A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: (1) on the training sessions to select optimal frequency bands for the feedback sessions and (2) on the feedback sessions to monitor changes.
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paper_title: Motor imagery and direct brain-computer communication

paper_content:

Motor imagery can modify the neuronal activity in the primary sensorimotor areas in a very similar way as observable with a real executed movement. One part of EEG-based brain-computer interfaces (BCI) is based on the recording and classification of circumscribed and transient EEG changes during different types of motor imagery such as, e.g., imagination of left-hand, right-hand, or foot movement. Features such as, e.g., band power or adaptive autoregressive parameters are either extracted in bipolar EEG recordings overlaying sensorimotor areas or from an array of electrodes located over central and neighboring areas. For the classification of the features, linear discrimination analysis and neural networks are used. Characteristic for the Graz BCI is that a classifier is set up in a learning session and updated after one or more sessions with online feedback using the procedure of "rapid prototyping." As a result, a discrimination of two brain states (e.g., leftversus right-hand movement imagination) can be reached within only a few days of training. At this time, a tetraplegic patient is able to operate an EEG-based control of a hand orthosis with nearly 100% classification accuracy by mental imagination of specific motor commands.
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paper_title: EEG-based communication and control: short-term role of feedback

paper_content:

When people learn to control the amplitudes of certain electroencephalogram (EEG) components (e.g., the 8-12 Hz /spl mu/-rhythm over sensorimotor cortex) and use them to move a cursor to a target on a video screen, feedback about performance is normally provided by cursor movement and by trial outcome (i.e., success or failure). The authors assessed the short-term effects of this feedback on EEG control. After subjects received initial training with feedback present, feedback was removed intermittently for periods of several minutes. Subjects still displayed EEG control when feedback was removed. Removal of cursor movement alone appeared to have effects comparable to removal of both cursor movement and trial outcome. These results show that, in the short-term at least, /spl mu/-rhythm control is not dependent on the sensory input provided by cursor movement. They also suggest that feedback can have inhibitory as well as facilitatory effects on EEG control, and that these effects vary across subjects. This finding has implications for the design of training procedures.
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paper_title: Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

paper_content:

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.
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paper_title: Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment

paper_content:

Abstract Objective : This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain–computer interface (BCI) for verbal communication. Methods : EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a ‘telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time–frequency maps of event-related (de-)synchronization (ERD/ERS). Results : The patient learned to ‘produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. ‘Copy spelling' was performed with a rate of approximately one letter per min. Conclusions : The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. ‘Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.
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paper_title: Learning to control brain rhythms: making a brain-computer interface possible

paper_content:

The ability to control electroencephalographic rhythms and to map those changes to the actuation of mechanical devices provides the basis for an assistive brain-computer interface (BCI). In this study, we investigate the ability of subjects to manipulate the sensorimotor mu rhythm (8-12-Hz oscillations recorded over the motor cortex) in the context of a rich visual representation of the feedback signal. Four subjects were trained for approximately 10 h over the course of five weeks to produce similar or differential mu activity over the two hemispheres in order to control left or right movement in a three-dimensional video game. Analysis of the data showed a steep learning curve for producing differential mu activity during the first six training sessions and leveling off during the final four sessions. In contrast, similar mu activity was easily obtained and maintained throughout all the training sessions. The results suggest that an intentional BCI based on a binary signal is possible. During a realistic, interactive, and motivationally engaging task, subjects learned to control levels of mu activity faster when it involves similar activity in both hemispheres. This suggests that while individual control of each hemisphere is possible, it requires more learning time.
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paper_title: AI-based classification of single-trial EEG data

paper_content:

This paper studies the potential of six artificial intelligence (AI)-based approaches for classification of non-averaged EEG data that is on important issue for the building of a brain-computer interface (BCI). The effectiveness of the methods in terms of classification accuracy, stability and speed is reported.
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paper_title: An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate

paper_content:

An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate /spl sigma/, the number of correctly spelled letters/min, up to /spl sigma/=3.38 (average /spl sigma/=1.99).
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paper_title: Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

paper_content:

Brain–computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.
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paper_title: Self-initiation of EEG-based communication in paralyzed patients

paper_content:

Abstract Objectives : Severely paralyzed patients could learn to voluntarily generate slow cortical potential (SCP) shifts in their electroencephalogram and to use these signals to operate a communication device. To enhance the patients' autonomy, the present study describes the development of a permanently available communication system that can be turned on and off by locked-in patients without external assistance. A skill necessary for turning the system on is the ability to regulate one's slow potentials in the absence of continuous feedback. Methods : A stepwise learning approach was employed to train two paralyzed patients to regulate their SCPs without continuous feedback. Elements of the original communication system were gradually removed and elements of the new stand-by mode were introduced. Results : At the end of the learning procedure, both patients achieved correct response rates of above 84% in training sessions without continuous feedback. This skill enabled them to turn the communication device on and off without assistance from others. Conclusions : These findings suggest that severely paralyzed individuals can learn to operate an EEG-based communication device autonomously.
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paper_title: Parametric models and spectral analysis for classification in brain-computer interfaces

paper_content:

Parametric modelling strategies and spectral analysis are explored in conjunction with linear discriminant analysis to facilitate an EEG based direct-brain interface for use by disabled people. A self-paced typing exercise is analysed by employing for feature extraction, respectively, an autoregressive model, an autoregressive with exogenous input model, and a time-frequency decomposition of the data. Modelling both the signal and noise is found to be more, effective than modelling the noise alone with the former yielding an accuracy of 70.7% and the latter an accuracy of 57.4%. Experiments, using the raw samples of a short-time power spectral density estimate of each trial as features, yielded an accuracy of 62.5%.
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paper_title: Timing of EEG-based cursor control.

paper_content:

In a gas insulated high voltage cable having an outer tubular, corrugated conductor and an inner centrally disposed conductor, a spacer construction for holding and spacing the inner conductor in the tubular outer conductor, wherein each spacer has at least two spacer elements of semi annular construction each and complementing each other for establishing a closed annulus, each element constructed for laterial slipping onto the inner conductor, and the two spacer elements are secured to each other to establish the closed annulus in positive disposition on the inner conductor. Additional features impede local voltage breakdown, positive interlock of parts and resilient load relief.
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paper_title: Classification of single trial EEG during motor imagery based on ERD

paper_content:

EEG-based brain computer interface (BCI) provides a completely new communication channel between human brain and computer. Classification of EEG signals is a difficult task, especially when the classification has to be preformed on a single-trial EEG to continuously control a device. Event related desynchronization (ERD) has proven to be induced on the contralateral sensorimotor area during imagination of a left or right hand movement. In this paper, we introduced a quantification of ERD, with which a lower classification error rate and a higher information transfer rate can be obtained. The performance was tested by the Graz dataset for BCI competition 2003.
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paper_title: A new approach to a brain-computer-interface (BCI) based on Learning Vector Quantization (LVQ3)

paper_content:

Planning and preparing voluntary finger movements result in a specific topographical pattern of event-related desynchronization (ERD) in the alpha band. These patterns generated for planning left and right hand movements show large enough differences to be seperable by a Learning Vector Quantizer (LVQ). This paper describes one-dimensional control of a cursor on a computer monitor based on the differentiation of two cortical activation patterns measured by two EEG channels.
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paper_title: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters

paper_content:

Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate, obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.
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paper_title: A new mode of communication between man and his surroundings

paper_content:

The feasibility of establishing an alternative mode of communication between man and his surroundings was studied. The form of communication proposed uses only the subject's brain waves, with no overt physical action required. The subject's electroencephalograms (EEG) were recorded while various mental tasks designed to elicit hemispheric responses were performed. Features formed from the EEG recording were then used as inputs into a Bayes quadratic classifier to test classification accuracy between the various tasks. The results obtained indicate that it is possible to accurately distinguish between any two of the five tasks investigated. A comparison between three different methods for creating the feature sets is also presented. >
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paper_title: "Virtual keyboard" controlled by spontaneous EEG activity

paper_content:

A "virtual keyboard" (VK) is a letter spelling device operated for example by spontaneous electroencephalogram (EEG), whereby the EEC is modulated by mental hand and leg motor imagery. We report on three able-bodied subjects, operating the VK. The ability in the use of the VK varies between 0.85 and 0.5 letters/min in error-free writing.
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paper_title: A local neural classifier for the recognition of EEG patterns associated to mental tasks

paper_content:

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former.
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paper_title: Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface

paper_content:

This paper describes a simple but very powerful method for feature selection. The Distinction Sensitive Learning Vector Quantizer (DSLVQ) is a learning classifier which focuses on relevant features according to its own instance based classifications. Two different experiments describe the application of DSLVQ as a feature selector for an EEG-based Brain Computer Interface (BCI) system. It is shown that optimal electrode positions as well as frequency bands are strongly dependent on each subject and that a subject specific feature selection is when important for BCI systems.
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paper_title: Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery.

paper_content:

EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback.
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paper_title: Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns

paper_content:

Linear and nonlinear (TDNN) models have been shown to estimate hand position using populations of action potentials collected in the pre-motor and motor cortical areas of a primate's brain. One of the applications of this discovery is to restore movement in patients suffering from paralysis. For real-time implementation of this technology, reliable and accurate signal processing models that produce small error variance in the estimated positions are required. In this paper, we compare the mapping performance of the FIR filter, gamma filter and recurrent neural network (RNN) in the peaks of reaching movements. Each approach has strengths and weaknesses that are compared experimentally. The RNN approach shows very accurate peak position estimations with small error variance.
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paper_title: Learning the contributions of the motor, premotor, and posterior parietal cortices for hand trajectory reconstruction in a brain machine interface

paper_content:

The ability to record, in real-time, the activity of hundreds of cortical neurons gives the ability to selectively study the function of clusters of cortical neurons in Brain Machine Interface (BMI) experiments. We have demonstrated using a recursive multilayer perceptron (RMLP) that using the appropriate signal processing theory in a well-chosen parsimonious model, we can develop constructs that agree with basic physiological modeling of neural control. By looking through the trained model, we have found interesting relationships between the neuronal firing and the movement. The RMLP allows us to continuously study the relationship between neural activity and behavior without the active interference of the experimenter. The findings presented in this study offer an opportunity for the neuroscience community to compare the cortical interactions as constructed by the RMLP to what is known about motor neurophysiology.
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paper_title: Timing of EEG-based cursor control.

paper_content:

In a gas insulated high voltage cable having an outer tubular, corrugated conductor and an inner centrally disposed conductor, a spacer construction for holding and spacing the inner conductor in the tubular outer conductor, wherein each spacer has at least two spacer elements of semi annular construction each and complementing each other for establishing a closed annulus, each element constructed for laterial slipping onto the inner conductor, and the two spacer elements are secured to each other to establish the closed annulus in positive disposition on the inner conductor. Additional features impede local voltage breakdown, positive interlock of parts and resilient load relief.
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paper_title: Optimal spatial filtering of single trial EEG during imagined hand movement

paper_content:

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. The authors demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.
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paper_title: FEATURE EXTRACTION IN DEVELOPMENT OF BRAIN-COMPUTER INTERFACE: A CASE STUDY

paper_content:

In this study we compared two spectral estimation methods for feature extraction in development of computer access for communication and control based on electroencephalographic (EEG) signals. We compared power spectrum calculated by fast Fourier transform (FFT) and auto regressive (AR) coefficients calculated by Burg's algorithm. We analyzed four surface EEG signals recorded above sensory- motor areas, while the subject was attempting to use only mental activities to modulate his EEG signals resulting in desired simple movements of animated object on the feedback computer screen. To establish communication channel based on EEG signals, we asked our subject to determine which mental activity produced reproducible control actions. During the on- line training we used AR coefficients of recorded EEG signals for the feedback generating variables, i.e. the object's movement direction and speed were dependent on the EEG pattern. Our subject was able to determine which mental activity resulted in desired movements and to learn to control object movements with more than 90% accuracy. We performed off-line analysis to determine if FFT or AR can be more successful in extracting relevant information from the EEG signals before performing pattern recognition by a machine learning classifier. Probability of misclassification was used as the outcome measure. Off-line analysis showed that the overall best classification accuracy achieved with AR exceeds that achieved with FFT.
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paper_title: Digital Biosignal Processing

paper_content:

Preface. Digital biosignal processing: a selective and subjective introduction. List of contributors. Chapter 1. Origins of the main human biosignals (R. Schandry). Chapter 2. Analog signal conditioning (T. Elbert). Chapter 3. Biosignal data acquisition (M. Buhrer and B. Sparrer). Chapter 4. The foundations of digital Fourier analysis (N.L. Hesselmann). Chapter 5. Digital signal conditioning (W. Wolf). Chapter 6. Model-based analysis of neurophysiological signals (B. Kemp and F.H. Lopes da Silva). Chapter 7. Time series analysis by means of linear modeling (B.H. Jansen). Chapter 8. Point processes. A signal processing approach to the analysis of event series in biomedical applications (G. Baselli and S. Civardi). Chapter 9. Models for estimation and removal of artifacts in biological signals (J.L. Kenemans, P.C.M. Molenaar and M.N. Verbaten). Chapter 10. Adaptive signal processing (J.I. Aunon and Z. Keirn). Chapter 11. Real time processing of event-related potentials (J.P. Rosenfeld). Chapter 12. Instrumental behavior shaping automata (G. Silverman and B.R. Dworkin). Chapter 13. Curvefitting (A.T. Johnson). Chapter 14. Wavelet detection and classification (A. Cohen). Chapter 15. Relationships among signals: cross-spectral analysis of the EEG (G. Dumermuth and L. Molinari). Chapter 16. Multivariate methods in biosignal analysis: application of principal component analysis to event-related potentials (J. Mocks and R. Verleger). Chapter 17. Mapping procedures (G. Pfurtscheller). Chapter 18. Digital image processing (K.D. Brinkmann, H.W. Eisermann and W. Heyne). Chapter 19. Neural network methods (I. Leuthausser). Subject index.
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paper_title: Extraction algorithms for cortical control of arm prosthetics

paper_content:

Abstract Now that recordings of multiple, individual action potentials are being made with chronic electrodes, it seems that previous work showing simple encoding of movement parameters in these spike trains can be used as a real-time control signal for prosthetic arms. Efficient extraction algorithms can compensate for the limited ensemble sample acquired with this emerging technology.
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paper_title: Frequency component selection for an EEG-based brain to computer interface

paper_content:

A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: (1) on the training sessions to select optimal frequency bands for the feedback sessions and (2) on the feedback sessions to monitor changes.
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paper_title: Asymptotics for and against cross-validation

paper_content:

SUMMARY The asymptotic consistency of cross-validatory assessment and the asymptotic efficiency of cross-validatory choice is investigated both in some generality and also in the context of particular applications.
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paper_title: Evaluating the performance of three feature sets for brain-computer interfaces with an early stopping MLP committee

paper_content:

We present preliminary classification results for a real time brain-computer interface. Our approach seeks to build individual brain interfaces rather than universal ones. This means that the interface should adapt to its owner; as it will incorporate a neural classifier that learns user-specific features. Three feature sets extracted with Fourier transform, autoregressive models and wavelets were evaluated with early stopping MLP committee. The goal was to classify EEG patterns related to imagined hand movements and relax. The best results were obtained with the autoregressive special features. The results so far are not satisfactory for their intended use as basis for robust EEG classification but they give us valuable basis for future work.
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paper_title: Automatic differentiation of multichannel EEG signals

paper_content:

Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. The authors present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform.
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paper_title: Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

paper_content:

Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as "yes" or "no" or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct "yes"/"no" BCI from a single session. Blind source separation (BSS) and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA) wrapped around a support vector machine (SVM) classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a "direct," single-session BCI.
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paper_title: Information transfer rate in a five-classes brain-computer interface

paper_content:

The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the EEG patterns is based on band power estimates and hidden Markov models. We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance.
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paper_title: Linear and nonlinear methods for brain-computer interfaces

paper_content:

At the recent Second International Meeting on Brain-Computer Interfaces (BCIs) held in June 2002 in Rensselaerville, NY, a formal debate was held on the pros and cons of linear and nonlinear methods in BCI research. Specific examples applying EEG data sets to linear and nonlinear methods are given and an overview of the various pros and cons of each approach is summarized. Overall, it was agreed that simplicity is generally best and, therefore, the use of linear methods is recommended wherever possible. It was also agreed that nonlinear methods in some applications can provide better results, particularly with complex and/or other very large data sets.
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paper_title: AI-based classification of single-trial EEG data

paper_content:

This paper studies the potential of six artificial intelligence (AI)-based approaches for classification of non-averaged EEG data that is on important issue for the building of a brain-computer interface (BCI). The effectiveness of the methods in terms of classification accuracy, stability and speed is reported.
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paper_title: Parametric models and spectral analysis for classification in brain-computer interfaces

paper_content:

Parametric modelling strategies and spectral analysis are explored in conjunction with linear discriminant analysis to facilitate an EEG based direct-brain interface for use by disabled people. A self-paced typing exercise is analysed by employing for feature extraction, respectively, an autoregressive model, an autoregressive with exogenous input model, and a time-frequency decomposition of the data. Modelling both the signal and noise is found to be more, effective than modelling the noise alone with the former yielding an accuracy of 70.7% and the latter an accuracy of 57.4%. Experiments, using the raw samples of a short-time power spectral density estimate of each trial as features, yielded an accuracy of 62.5%.
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paper_title: EEG-based neuroprosthesis control: A step towards clinical practice

paper_content:

Abstract This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain–Computer Interface (BCI) with an implanted neuroprosthesis (Freehand ® system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand ® system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain–Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.
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paper_title: EEG-based communication: presence of an error potential

paper_content:

Abstract Background : EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect. Methods : In an effort to develop a method for detecting errors, this study compared the EEG immediately after correct target selection to that after incorrect selection. Results : The data showed that a mistake is followed by a positive potential centered at the vertex that peaks about 180 ms after the incorrect selection. Conclusion : The results suggest that this error potential might provide a method for detecting and voiding errors that requires no additional time and could thereby improve the speed and accuracy of EEG-based communication.
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paper_title: Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

paper_content:

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.
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paper_title: Evaluation of spike-detection algorithms fora brain-machine interface application

paper_content:

Real time spike detection is an important requirement for developing brain machine interfaces (BMIs). We examined three classes of spike-detection algorithms to determine which is best suited for a wireless BMI with a limited transmission bandwidth and computational capabilities. The algorithms were analyzed by tabulating true and false detections when applied to a set of realistic artificial neural signals with known spike times and varying signal to noise ratios. A design-specific cost function was developed to score the relative merits of each detector; correct detections increased the score, while false detections and computational burden reduced it. Test signals both with and without overlapping action potentials were considered. We also investigated the utility of rejecting spikes that violate a minimum refractory period by occurring within a fixed time window after the preceding threshold crossing. Our results indicate that the cost-function scores for the absolute value operator were comparable to those for more elaborate nonlinear energy operator based detectors. The absolute value operator scores were enhanced when the refractory period check was used. Matched-filter-based detectors scored poorly due to their relatively large computational requirements that would be difficult to implement in a real-time system.
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paper_title: Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring

paper_content:

We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms.
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paper_title: Analyzing Trends in Brain Interface Technology: A Method to Compare Studies

paper_content:

Continued progress in the field of Brain Interface (BI) research has encouraged the rapid expansion of the BI community over the last two decades. As the number of BI researchers and organizations steadily increases, newer and more advanced technologies are constantly produced, evaluated, and reported. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the diversity of the field has hindered the development of objective methods of comparison. This paper introduces a new method for directly comparing studies of BI technology based on the theoretical models and taxonomy proposed by Mason, Moore, and Birch. The effectiveness of the proposed method was demonstrated by interpreting and comparing a representative set of 21 BI studies. The method allowed us to 1) identify the salient aspects of a specific BI study, 2) identify what has been reported and what has been omitted, 3) facilitate a complete and objective comparison with other studies, and 4) characterize overall trends, areas of inactivity, and reporting practices.
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Title: A Survey of Signal Processing Algorithms in Brain-Computer Interfaces Based on Electrical Brain Signals

Section 1: Introduction
Description 1: Briefly introduce the purpose of brain-computer interfaces (BCIs), the significance of signal processing in BCI design, and the aims of the paper.

Section 2: General structure of a BCI system
Description 2: Describe the components of a BCI system, including how electrical brain signals are recorded, processed, and translated into control commands.

Section 3: Electrophysiological sources of control in current BCIs
Description 3: Discuss various electrophysiological sources and neuromechanisms used in BCIs, and categorize BCI systems based on these sources.

Section 4: Methods
Description 4: Detail the methodology used for selecting and reviewing the BCI designs included in the survey.

Section 5: Results
Description 5: Summarize the findings of the survey in terms of the signal processing techniques used in different components of BCI designs, as illustrated in tables.

Section 6: Discussion
Description 6: Provide an in-depth discussion on the most commonly used signal processing methods, their advantages, limitations, and performance in BCI systems.

Section 7: Conclusion
Description 7: Conclude with a summary of the key findings from the survey, the importance of method comparison for different BCI systems, and future directions for research.