A Survey of Stimulation Methods Used in SSVEP-Based BCIs
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paper_title: A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication

paper_content:

In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.
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paper_title: Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena

paper_content:

The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent ob- ject, its features have to be bound together. This was found to be achieved in cat and monkey brains by tem- poral correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30-80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscilla- tions at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phe- nomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.
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paper_title: Brain–computer interfaces for communication and control

paper_content:

Abstract For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world – a brain–computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or ‘locked in’, with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10–25 bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
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paper_title: A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals

paper_content:

Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files
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paper_title: Low level control in a semi-autonomous rehabilitation robotic system via a Brain-Computer Interface

paper_content:

In this work, a connection between a semi-autonomous rehabilitation robotic system and Brain-Computer Interfaces (BCI) is described This paper focuses on a system for user intervention in low-level movement control of an assistive robotic arm. The rehabilitation robotic system allows tetra-plegics to control the system with high-level commands (e.g., "grab the bottle"), and then to intervene in the execution of the task, if they see that something is going wrong. In such a case, the user gets the opportunity to continue the task with a low-level control of the robot arm. A system for such a control on a low abstraction level by a Brain-Computer Interface based on P300 and steady-state visual evoked potentials (SSVEP) will be described in this work.
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paper_title: Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system

paper_content:

This paper describes the development of a synchronous, online brain computer interface (BCI) system based on detecting the steady-state visual evoked potential (SSVEP). The system includes a programmable visual stimulator, EEG amplifier with filter system, data acquisition card, and signal processing and classification algorithms. Two types of experiments were carried out; training experiments were conducted to determine three optimal frequencies for each and every subject. For the testing experiments, three visual stimuli were presented simultaneously to the subject. Subject was required to focus his/her attention only on one of the target stimulus, and the system will detect the targeted stimulus the subject was focusing on. Five subjects have participated in the study with average detection accuracy of 83.10%.
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paper_title: A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication

paper_content:

In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.
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paper_title: Brain-Computer Interface for high-level control of rehabilitation robotic systems

paper_content:

In this work, a brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is presented as an input device for the human machine interface (HMI) of the semi-autonomous robot FRIEND II. The role of the BCI is to translate high-level requests from the user into control commands for the FRIEND II system. In the current application, the BCI is used to navigate a menu system and to select commands such as pouring a beverage into a glass. The low-level control of the test platform, the rehabilitation robot FRIEND II, is executed by the control architecture MASSiVE, which in turn is served by a planning instance, an environment model and a set of sensors (e.g., machine vision) and actors. The BCI is introduced as a step towards the goal of providing disabled users with at least 1.5 hours independence from care givers.
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paper_title: Multiple color stimulus induced steady state visual evoked potentials

paper_content:

Steady state visual evoked potentials (SSVEP) are of the characteristics of high SNR and effectiveness in short-term identification of evoked responses. In most of the SSVEP experiments, single high frequency stimuli are used. To characterize the complex rhythms in SSVEP, a new multiple color stimulus pattern is proposed in this paper. FFT and bispectrum analysis methods are used to detect the phase coupling of the two stimulation frequencies in the recorded SSVEP. The results show that the rhythms at the duplation, sum or difference of the two-stimulation frequencies can be evoked by multiple color stimulus.
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paper_title: An EEG-based cursor control system

paper_content:

Describes an experimental brain-computer interface (BCI) which allows severely disabled persons to move the cursor on the screen. The system is based on the detecting of steady-state visual evoked potential (SSVEP). Four rectangular blocks around the cursor indicate four directions and flash at different frequencies on the screen. Once the subject fixates on one of the blocks, the specific SSVEP is obtained SSVEP signal is processed online to determine which block the eyes are focusing. As a feedback, the cursor can be moved in corresponding directions. Preliminary results are presented and evaluated.
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paper_title: A four command BCI system based on the SSVEP protocol.

paper_content:

This paper discusses the development of a four command BCI system. This system is composed of a wearable electroencephalogram acquisition unit interfaced to a computer by a wireless Bluetooth® (BT) connection. The implemented system relies on the SSVEP protocol applied to a four selection system. In order to achieve the maximum reliability against false positives a five class classifier was used considering the idle state as an independent class. In order to maximize the usability of the system a two channel solution was tested and adopted. The BCI algorithm was based on a supervised multi- class classifier implemented by combining different binary Regularized Linear Discriminant Analysis (RLDA) classifiers. The biofeedback was evaluated by combining the resultant time signed distance with quality index related to the number of coherent identification obtained with the one-vs-all approach.
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paper_title: Application and Contrast in Brain-Computer Interface between Hilbert-Huang Transform and Wavelet Transform

paper_content:

Brain-computer interface (BCI) can make people control machines through electroencephalogram (EEG) which produced by brain activities. It provides a new communication method between human and environment and extents human's ability to control machines. One of the key points of BCI system is how to abstract and distinguish different EEG features. Therefore, EEG signal processing method is the focus of BCI. This article analyzed Wavelet Transform method and Hilbert-Huang Transform (HHT) method. The results indicate that both these two methods can abstract the main characters of the EEG. But HHT can more accurately express EEG distribution in time and frequency domain. That's because it can produce a self-adaptive basis according to the signal data and obtain local and instantaneous frequency of EEG.
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paper_title: Stimulator selection in SSVEP-based BCI

paper_content:

Steady-state visual evoked potentials (SSVEP) are increasingly used in the development of brain-computer interface techniques (BCI). We investigated the spectrum differences of three kinds of flickers and the differences in SSVEPs evoked by three different stimulators, i.e. the light-emitting diode, the cathode ray tube of a desktop monitor and the liquid crystal display of a laptop screen. The results showed that the SSVEP differences were strongly related to the frequency spectrum differences of the flickers. According to these differences, the stimulator was selected based on the complexity of the BCI system.
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paper_title: EEG-based control for human-computer interaction

paper_content:

An interface whereby brain responses can control machines has been developed by the Armstrong Laboratory. This EEG-based control uses the magnitude of the steady-state visual evoked response (SSVER) as a control signal. The SSVER is identified and monitored using non-invasive scalp electrodes and advanced signal processing technology. With biofeedback, users learn to increase or decrease the magnitude of the VSYER to an evoking stimulus. These responses are translated into commands that control the operation of a physical device or computer program. After further development this innovative interface could revolutionize human interaction with complex systems.
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paper_title: A comparison of covert and overt attention as a control option in a steady-state visual evoked potential-based brain computer interface

paper_content:

EEG data were recorded from occipital scalp regions of subjects who attended to an alternating checkerboard stimulus in one visual field while ignoring a similar stimulus of a different frequency in the opposite visual field. Classification of left/right spatial attention is attempted by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli to assess the potential use of such a spatial selective attention paradigm in a brain computer interface (BCI). Experimental setup and analysis procedure in a previous study in which eye movement is permitted are replicated in order to quantify differences in classification performance using overt and covert attention. Four variations of the basic paradigm, involving both feedback and addition of extra mental load, are studied for comparison. The average accuracy is found to be reduced by /spl sim/20% in the switch from overt to covert attention when no other specifications of the task are changed.
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paper_title: The SSVEP topographic scalp maps by Canonical correlation analysis

paper_content:

As the number of electrodes increases, topographic scalp mapping methods for electroencephalogram (EEG) data analysis are becoming important. Canonical correlation analysis (CCA) is a method of extracting similarity between two data sets. This paper presents an EEG topographic scalp mapping -based CCA for the steady-state visual evoked potentials (SSVEP) analysis. Multi-channel EEG data and the sinusoidal reference signal were used as the inputs of CCA. The output linear combination was then employed for mapping. Our experimental results prove the topographic scalp mapping-based CCA can instruct for the improvement of SSVEP-based brain computer interface (BCI) system.
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paper_title: Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI

paper_content:

Abstract Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) have been investigated increasingly in the last years. This type of brain signals resulting from repetitive flicker stimulation has the same fundamental frequency as the stimulation including higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by localizing individual electroencephalogram (EEG) recording positions. In the current work, a 4-class SSVEP-based BCI system was set up. Ten subjects participated and EEG was recorded from 21 channels overlying occipital areas. Features were extracted by applying Discrete Fourier transformation and a lock-in analyzer system. A simple one versus the rest classifier was applied to compare methods and localize individual electrode positions. It was shown that the use of three SSVEP-harmonics recorded from individual channels yielded significantly higher classification accuracy compared to one harmonic and to the standard positions O1 and O2. Furthermore, the application of a simple one versus the rest classifier and the use of a lock-in analyzer system lead to a higher classification accuracy (mean ± S.D., about 74 ± 16%) in a 4-class BCI compared to the commonly used Discrete Fourier transformation (DFT, 62 ± 14%). By applying a screening procedure, the optimal electrode positions for bipolar derivations can be detected. Furthermore, information about subject's specific ‘resonance-like’ frequency regions can be obtained by observing higher harmonics of the SSVEPs.
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paper_title: Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs

paper_content:

Due to the relative noise and artifact insensitivity, steady-state visual evoked potential (SSVEP) has been used increasingly in the study of a brain–computer interface (BCI). However, SSVEP is still influenced by the same frequency component in the spontaneous EEG, and it is meaningful to find a parameter that can avoid or decrease this influence to improve the transfer rate and the accuracy of the SSVEP-based BCI. In this work, with wavelet analysis, a new parameter named stability coefficient (SC) was defined to measure the stability of a frequency, and then the electrode with the highest stability was selected as the signal electrode for further analysis. After that, the SC method and the traditional power spectrum (PS) method were used comparatively to recognize the stimulus frequency from an analogous BCI data constructed from a real SSVEP data, and the results showed that the SC method is better for a short time window data.
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paper_title: A BCI-based environmental controller for the motion-disabled

paper_content:

With the development of brain-computer interface (BCI) technology, researchers are now attempting to put current BCI techniques into practical application. This paper presents an environmental controller using a BCI technique based on steady-state visual evoked potential. The system is composed of a stimulator, a digital signal processor, and a trainable infrared remote-controller. The attractive features of this system include noninvasive signal recording, little training requirement, and a high information transfer rate. Our test results have shown that this system can distinguish at least 48 targets and provide a transfer rate up to 68 b/min. The system has been applied to the control of an electric apparatus successfully.
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paper_title: Towards an independent brain–computer interface using steady state visual evoked potentials

paper_content:

Abstract Objective Brain–computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. Methods Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. Results Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. Conclusions The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI’s dependence on muscle activity is a function of many factors, including the display, task, environment, and user. Significance SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.
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paper_title: Fast Multi-command SSVEP Brain Machine Interface without Training

paper_content:

We propose a new multi-stage procedure for a real time brain machine/computer interface (BMI) based on the Steady State Visual Evoked Potentials (SSVEP) paradigm elicited by means of flickering checkerboards. The developed system work in asynchronous mode and it does not require training phase and its able to detect fast multiple independent visual commands. Up to 8 independent commands, were tested at the presented work and the proposed BMI system could be extended to more independent commands easily. The system has been extensively experimented with 4 young healthy subjects, confirming the high performance of the proposed procedure and its robustness in respect to artifacts.
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paper_title: A Wearable Home BCI system: preliminary results with SSVEP protocol

paper_content:

This paper presents and discusses the realization and the performances of a wearable system for EEG-based BCI applications. The system (called Kimera) consists of a two-layer hardware architecture (the wireless acquisition and transmission board based on a BluetoothregARM chip, and a low power miniaturized biosignal acquisition analog front end) together with a software suite (called Bellerophonte) for the graphic user interface management, protocol execution, data recording, transmission and processing. The implemented BCI system was based on the SSVEP protocol, applied to a two state selection by using standards display/monitor with a couple of high efficiency LEDs. The frequency features of the signal were computed and used in the intention detection. The BCI algorithm is based on a supervised classifier implemented through a multi-class canonical discriminant analysis (CDA) with a continuous real-time feedback based on the mahalanobis distance parameter. Five healthy subjects participated in the first phase for a preliminary device validation. The obtained results are very interesting and promising, being lined out to the most recent performance reported in literature with a significant improvement both in system and in classification capabilities. The user-friendliness and low cost of the Kimera & Bellerophonte platform make it suitable for the development of home BCI applications
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paper_title: USING COMB FILTER TO ENHANCE SSVEP FOR BCI APPLICATIONS

paper_content:

It is postulated that by proper selection of the stimulus frequency in SSVEP BCI systems and comb filtering of the EEG signal prior to performing the DFT, substantial increase of the SSVEP signal-to-noise ratio can be achieved. This enhancement may contribute to the speed and accuracy increase of brain-computer interfaces. Experimental results are presented to confirm the comb filter usefulness.
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paper_title: A Virtual Keypad Based on Alternate Half-Field Stimulated Visual Evoked Potentials

paper_content:

A virtual 8-key keypad has been designed and constructed to experimentally validate a new kind of noninvasive brain computer interface (BCI). The principle of its operation is based on a technique of alternate visual half-field stimulation, proposed recently by second of the authors. Microcontroller and DSP integrated circuits are used to generate the signals driving light-emitting diodes for visual stimulation and to detect brain response in the EEG signal. The EEG signal is measured from 3 electrodes placed on occipital part of the scalp using a battery- operated amplifier and A/C converter. A Bluetooth chip is used to transmit the stream of measured data to the processor. Tests of the keypad operation, involving 10 volunteers, show it is faster than conventional BCI devices based on steady-state visual evoked potentials, reported so far in the literature.
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paper_title: User adaptive BCIs: SSVEP and P300 based interfaces

paper_content:

Brain Computer Interfaces (BCI) represent a new communication option for those suffering from neuromuscular impairment that prevents them from using conventional augmented communication methods. This new approach has been developing quickly during the last few years, thanks to the increasing of computational power and the new algorithms for signal processing (Independent Component Analysis, Wavelets decomposition, Support Vector Machine etc.) that can be applied to the studies made on brain waves. Here follows two methodologies of approach based on making the computer adapt to the human brain activity and not vice-versa. The P300 and the SSVEP based BCIs, here presented, have the characteristics of not demanding specific training to the user.
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paper_title: An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method

paper_content:

In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain–computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bit min−1. The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.
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paper_title: Alternate half-field stimulation technique for SSVEP-based brain-computer interfaces

paper_content:

A technique of half-field alternate visual stimulation, combined with differential EEG signal measurement, is applied to acquire steady-state brain-evoked signals. Taking the difference of two signals, measured with properly placed electrodes, enhances the visual-evoked potential (VEP) and suppresses the noise components. An array of different-flash-frequency light-emitting-diode (LED) pairs forms a multiple-choice table. By fixating at different LED pairs, the user communicates (by the VEP of corresponding frequency) his/her decision about the selection of the table entry.
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paper_title: Multi-Command Real-Time Brain Machine Interface Using SSVEP: Feasibility Study for Occipital and Forehead Sensor Locations

paper_content:

We propose a new multi-stage procedure for a real time brain machine/computer interface (BMI) based on the Steady State Visual Evoked Potentials (SSVEP) paradigm. The developed system allows a BMI user to navigate a small car (or any other object) on the computer screen in near real time, in any of the four directions. Extensive experiments with 4 young healthy subjects for two different electrode configurations (Occipital/Forehead), confirmed the high performance of the proposed on-line BMI system.
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paper_title: Multiple color stimulus induced steady state visual evoked potentials

paper_content:

Steady state visual evoked potentials (SSVEP) are of the characteristics of high SNR and effectiveness in short-term identification of evoked responses. In most of the SSVEP experiments, single high frequency stimuli are used. To characterize the complex rhythms in SSVEP, a new multiple color stimulus pattern is proposed in this paper. FFT and bispectrum analysis methods are used to detect the phase coupling of the two stimulation frequencies in the recorded SSVEP. The results show that the rhythms at the duplation, sum or difference of the two-stimulation frequencies can be evoked by multiple color stimulus.
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paper_title: Optimization Strategy for SSVEP-Based BCI in Spelling Program Application

paper_content:

This paper describes an optimization strategy for steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI), especially in a spelling program application. In this application, there are at least three components for implementing a complete BCI application: stimulator, signal processing, and application (spelling program). Ideally, those three components should run on different processing units in order to obtain optimum performance. But integrating those three components in one computer system also gives advantages: make it easier for the subject to concentrate and simplifies the system configuration. There are two main parts that need to be optimized: the flickering animation and the spelling system. When optimizing the flickering animation, we will focus on the display driver technology and programming aspects. The optimization of spelling system will be focused on the layout and representation of the letter matrix. We tested our program on several computers for the following parameters: frequency range, frequency resolution, and frequency stability. It can be concluded that using a computer monitor as the stimulator, the maximum synthesizable stimulator frequency is always half of its minimum refresh-rate, no matter which software technology is applied (DirectX or OpenGL). The maximum synthesizable frequency of up to 30 Hz with frequency resolution 0.11Hz is achieved. We have tested our system on 106 subjects during CeBIT 2008 in Hannover, Germany. Mean accuracy for the spelling system is 92.5%. Therefore, the optimization strategy described here led to a stable and reliable system that performed effectively across most subjects without requiring extensive expert help or expensive hardware.
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paper_title: Application and Contrast in Brain-Computer Interface between Hilbert-Huang Transform and Wavelet Transform

paper_content:

Brain-computer interface (BCI) can make people control machines through electroencephalogram (EEG) which produced by brain activities. It provides a new communication method between human and environment and extents human's ability to control machines. One of the key points of BCI system is how to abstract and distinguish different EEG features. Therefore, EEG signal processing method is the focus of BCI. This article analyzed Wavelet Transform method and Hilbert-Huang Transform (HHT) method. The results indicate that both these two methods can abstract the main characters of the EEG. But HHT can more accurately express EEG distribution in time and frequency domain. That's because it can produce a self-adaptive basis according to the signal data and obtain local and instantaneous frequency of EEG.
---
paper_title: Stimulator selection in SSVEP-based BCI

paper_content:

Steady-state visual evoked potentials (SSVEP) are increasingly used in the development of brain-computer interface techniques (BCI). We investigated the spectrum differences of three kinds of flickers and the differences in SSVEPs evoked by three different stimulators, i.e. the light-emitting diode, the cathode ray tube of a desktop monitor and the liquid crystal display of a laptop screen. The results showed that the SSVEP differences were strongly related to the frequency spectrum differences of the flickers. According to these differences, the stimulator was selected based on the complexity of the BCI system.
---
paper_title: A comparison of covert and overt attention as a control option in a steady-state visual evoked potential-based brain computer interface

paper_content:

EEG data were recorded from occipital scalp regions of subjects who attended to an alternating checkerboard stimulus in one visual field while ignoring a similar stimulus of a different frequency in the opposite visual field. Classification of left/right spatial attention is attempted by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli to assess the potential use of such a spatial selective attention paradigm in a brain computer interface (BCI). Experimental setup and analysis procedure in a previous study in which eye movement is permitted are replicated in order to quantify differences in classification performance using overt and covert attention. Four variations of the basic paradigm, involving both feedback and addition of extra mental load, are studied for comparison. The average accuracy is found to be reduced by /spl sim/20% in the switch from overt to covert attention when no other specifications of the task are changed.
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paper_title: A real-time brain-computer interface based on steady-state visual evoked potentials

paper_content:

Steady-state visual evoked potential (SSVEP)-based BCIs take advantage of the stereotypical response to flicker in the visual cortex. They offer the possibility of high performance non-invasively and with minimal training time. A real-time SSVEP BCI system was developed and tested on 14 control subjects. While intersubject variability existed, the results were promising, proving that the stimulation frequency of gaze can be detected in under five seconds. This detection can be made even when nine or more flickering stimuli are simultaneously present.
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paper_title: Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment

paper_content:

This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.
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paper_title: A practical VEP-based brain-computer interface

paper_content:

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.
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paper_title: Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

paper_content:

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed
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paper_title: The effect of spatial frequency on chromatic and achromatic steady-state visual evoked potentials

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Abstract Objective : Little is known about the physiological properties of the major components of steady-state visual evoked potentials (VEPs). Based on the hypothesis that isoluminant color and high contrast pattern differentially activate the parvo- and magnocellular pathways, we studied difference in spatial frequency function between chromatic and achromatic VEPs to sinusoidal gratings. Methods : Steady-state VEPs to isoluminant chromatic (red-green) and high contrast (90%) achromatic (black-white) sinusoidal gratings with nine spatial frequencies (0.5 to 8.0 cycles/° (cpd)) at 4 Hz (8 reversals/s) were recorded in 13 normal subjects. VEPs were Fourier analyzed to obtain phase and amplitude of the second (2F) and fourth (4F) harmonic responses. Results : The 2F amplitude of chromatic VEPs decreased above 4.0 cpd in a low-pass function while that of achromatic VEPs showed a band-pass function with a peak at 4.0 cpd. The 4F amplitude of chromatic VEPs was not affected significantly by spatial frequency whereas that of achromatic VEPs exhibited a high-pass function. The phases of 2F and 4F showed a non-monotonic function of spatial frequency in both chromatic and achromatic stimuli with peaks at middle spatial frequencies. Conclusion : Chromatic and achromatic visual stimuli differently affected 2F and 4F components, which thus suggests that 2F and 4F components are generated from different neuronal subgroups largely in the parvocellular pathway.
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paper_title: A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals

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Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files
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paper_title: Sustained division of the attentional spotlight

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By voluntarily directing attention to a specific region of a visual scene, we can improve our perception of stimuli at that location1. This ability to focus attention upon specific zones of the visual field has been described metaphorically as a moveable spotlight or zoom lens that facilitates the processing of stimuli within its ‘beam’2,3. A long-standing controversy has centred on the question of whether the spotlight of spatial attention has a unitary beam or whether it can be divided flexibly to disparate locations2,4,5,6. Evidence supporting the unitary spotlight view has come from numerous behavioural3,7,8,9,10 and electrophysiological11,12 studies. Recent experiments, however, indicate that the spotlight of spatial attention may be divided between non-contiguous zones of the visual field for very brief stimulus exposures (<100 ms)13,14. Here we use an electrophysiological measure of attentional allocation (the steady-state visual evoked potential) to show that the spotlight may be divided between spatially separated locations (excluding interposed locations) over more extended time periods. This spotlight division appears to be accomplished at an early stage of visual-cortical processing.
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paper_title: Fast Multi-command SSVEP Brain Machine Interface without Training

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We propose a new multi-stage procedure for a real time brain machine/computer interface (BMI) based on the Steady State Visual Evoked Potentials (SSVEP) paradigm elicited by means of flickering checkerboards. The developed system work in asynchronous mode and it does not require training phase and its able to detect fast multiple independent visual commands. Up to 8 independent commands, were tested at the presented work and the proposed BMI system could be extended to more independent commands easily. The system has been extensively experimented with 4 young healthy subjects, confirming the high performance of the proposed procedure and its robustness in respect to artifacts.
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paper_title: Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

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We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.
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paper_title: A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces

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The objective is to increase the number of selections in brain computer interfaces (BCI) by recording and analyzing the steady state visual evoked potential response to dual stimulation. A BCI translates the VEP signals into user commands. The frequency band from which stimulation frequency can be selected is limited for SSVEP. This paper discusses a method to increase the number of commands by using a suitable combination of frequencies for stimulation. A biopotential amplifier based on the driven right leg circuit (DRL) is used to record 60 s epochs of the SSVEP (Oz-A1) on 15 subjects using simultaneous overlapped stimulation (6, 7, 12, 13 and 14 Hzs and corresponding half frequencies). The power spectrum of each recording is obtained by frequency domain averaging of 400 ms SSVEPs and the spectral peaks were normalized. The spectral peaks of the combination frequencies of stimulation are predominant compared to individual stimulating frequencies. This method increases the number of selections by using a limited number of stimulating frequencies in BCI. For example, six selections are possible by generating only three frequencies.
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paper_title: Phase Coherent Detection of Steady-State Evoked Potentials: Experimental Results and Application to Brain-Computer Interfaces

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Steady-state visual evoked potentials (SSVEP) have frequently been used in brain-computer interfaces (BCI). These BCIs commonly use non-coherent estimators for the discrimination of different visual stimuli. Here we present a novel phase coherent detection method for discrimination. This method utilizes information about the stimulus phase which leads to a significant reduction in classification error. In addition, using stimuli with identical reversal rate but different phases allow for a more flexible BCI system design. In this study, EEG-signals from two electrodes were recorded while subjects gazed at one out of two stimuli. These stimuli differed in reversal rate and/or relative phase shift. We compared the results obtained with the conventional non-coherent estimator with our phase coherent estimator in an offline analysis for different stimulus conditions. We found that including phase information into the analysis can reduce error probability by at least a factor of two. In addition we showed that stimuli of identical frequency that differed in phase, only, can reliably be discriminated. Similar low error probabilities as for stimuli that differed in phase and frequency were found
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paper_title: Alternate half-field stimulation technique for SSVEP-based brain-computer interfaces

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A technique of half-field alternate visual stimulation, combined with differential EEG signal measurement, is applied to acquire steady-state brain-evoked signals. Taking the difference of two signals, measured with properly placed electrodes, enhances the visual-evoked potential (VEP) and suppresses the noise components. An array of different-flash-frequency light-emitting-diode (LED) pairs forms a multiple-choice table. By fixating at different LED pairs, the user communicates (by the VEP of corresponding frequency) his/her decision about the selection of the table entry.
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paper_title: Multiple color stimulus induced steady state visual evoked potentials

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Steady state visual evoked potentials (SSVEP) are of the characteristics of high SNR and effectiveness in short-term identification of evoked responses. In most of the SSVEP experiments, single high frequency stimuli are used. To characterize the complex rhythms in SSVEP, a new multiple color stimulus pattern is proposed in this paper. FFT and bispectrum analysis methods are used to detect the phase coupling of the two stimulation frequencies in the recorded SSVEP. The results show that the rhythms at the duplation, sum or difference of the two-stimulation frequencies can be evoked by multiple color stimulus.
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paper_title: The cognitive electrophysiology of mind and brain

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Publisher Summary The event-related potentials (ERPs) of the brain are wave forms reflecting brain voltage fluctuations in time. This chapter highlights cognitive electrophysiology as a well-established field of science. The main assumption of cognitively oriented electrophysiological research is that cognition is implemented in the brain through physiological changes. Traditionally, for more than 100 years, cognitive and neurophysiological processes in humans have been studied by psychophysical and behavioral methods. The difficulty in differentiating cognition from brain localization is not, however, unique to neuroimaging and electrophysiological studies.
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paper_title: Feature-selective attention enhances color signals in early visual areas of the human brain

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We used an electrophysiological measure of selective stimulus processing (the steady-state visual evoked potential, SSVEP) to investigate feature-specific attention to color cues. Subjects viewed a display consisting of spatially intermingled red and blue dots that continually shifted their positions at random. The red and blue dots flickered at different frequencies and thereby elicited distinguishable SSVEP signals in the visual cortex. Paying attention selectively to either the red or blue dot population produced an enhanced amplitude of its frequency-tagged SSVEP, which was localized by source modeling to early levels of the visual cortex. A control experiment showed that this selection was based on color rather than flicker frequency cues. This signal amplification of attended color items provides an empirical basis for the rapid identification of feature conjunctions during visual search, as proposed by “guided search” models.
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paper_title: Visual spatial attention control in an independent brain-computer interface

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This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.
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paper_title: The power of human brain magnetoencephalographic signals can be modulated up or down by changes in an attentive visual task

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This paper presents evidence indicating that the signals generated by neural responses to visual input can be either enhanced by increasing or suppressed by decreasing the area of the stimuli to which attention is directed. We used magnetoencephalography (MEG) to measure the frequency-tagged steady-state visual evoked responses of 11 subjects presented with two superimposed images flickering at different frequencies. Each image consisted of seven parallel bars of equal length; in any image, all bars were either red or green and either horizontal or vertical. At randomly chosen times during the experiments, any one of the three middle bars in either image transiently increased or decreased in width. Subjects were asked to attend to one image and ignore the other and to respond to changes in bar width in the attended image with a key press. In one condition, subject responses were required for changes in any of the three central bars of the attended image. We found that visual steady-state evoked responses to the attended image were enhanced relative to those evoked by the unattended image in this condition. In a second condition, subject responses were required for changes only in the middle bar. In this condition, the responses to the attended image were suppressed relative to those of the unattended image. These results may reflect relative differences in the synchronization and desynchronization of responding neuronal populations.
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paper_title: Towards an independent brain–computer interface using steady state visual evoked potentials

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Abstract Objective Brain–computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. Methods Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. Results Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. Conclusions The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI’s dependence on muscle activity is a function of many factors, including the display, task, environment, and user. Significance SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.
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paper_title: Brain–computer interfaces for communication and control

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Abstract For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world – a brain–computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or ‘locked in’, with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10–25 bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
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paper_title: Stimulator selection in SSVEP-based BCI

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Steady-state visual evoked potentials (SSVEP) are increasingly used in the development of brain-computer interface techniques (BCI). We investigated the spectrum differences of three kinds of flickers and the differences in SSVEPs evoked by three different stimulators, i.e. the light-emitting diode, the cathode ray tube of a desktop monitor and the liquid crystal display of a laptop screen. The results showed that the SSVEP differences were strongly related to the frequency spectrum differences of the flickers. According to these differences, the stimulator was selected based on the complexity of the BCI system.
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Title: A Survey of Stimulation Methods Used in SSVEP-Based BCIs

Section 1: Introduction
Description 1: Introduce the concept of SSVEP-based BCIs and the relevance of stimulation methods in their performance.

Section 2: Repetitive Visual Stimuli
Description 2: Discuss the different types of repetitive visual stimuli used in SSVEP-based BCIs.

Section 3: Literature Search and Inclusion Criteria
Description 3: Describe the methodology for conducting the literature survey and the criteria for including papers in the review.

Section 4: State of the Art
Description 4: Provide a detailed categorization of currently used repetitive visual stimuli and their properties.

Section 5: Discussion
Description 5: Discuss the effects of different repetitive visual stimuli on SSVEP response and present innovative stimulation designs.

Section 6: Conclusion
Description 6: Summarize the findings regarding stimulation types, frequencies, and colors, and their impact on SSVEP-based BCIs.