Nikolay V. Manyakov

Comparison of classification methods for P300 brain-computer interface on disabled subjects

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patients disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface

A P300 Speller is a brain-computer interface (BCI) that enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms (EEG). This BCI application is of particular interest to disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrPs) in the EEG are generated by the subjects perception of an error. We report on the possibility of using these ErrPs for improving the performance of a P300 Speller. Overall nine subjects were tested, allowing us to study their EEG responses to correct and incorrect performances of the BCI, compare our findings to previous studies, explore the possibility of detecting ErrPs and discuss the integration of ErrP classifiers into the P300 Speller system.

Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device

In this paper, we introduce a game in which the player navigates an avatar through a maze by using a brain-computer interface (BCI) that analyzes the steady-state visual evoked potential (SSVEP) responses recorded with electroencephalography (EEG) on the players scalp. The four-command control game, called The Maze, was specifically designed around an SSVEP BCI and validated in several EEG setups when using a traditional electrode cap with relocatable electrodes and a consumer-grade headset with fixed electrodes (Emotiv EPOC). We experimentally derive the parameter values that provide an acceptable tradeoff between accuracy of game control and interactivity, and evaluate the control provided by the BCI during gameplay. As a final step in the validation of the game, a population study on a broad audience was conducted with the EPOC headset in a real-world setting. The study revealed that the majority (85%) of the players enjoyed the game in spite of its intricate control (mean accuracy 80.37%, mean mission time ratio 0.90). We also discuss what to take into account while designing BCI-based games.

MULTICHANNEL DECODING FOR PHASE-CODED SSVEP BRAIN-COMPUTER INTERFACE

We propose a complex-valued multilayer feedforward neural network classifier for decoding of phase-coded information from steady-state visual evoked potentials. To optimize the performance of the classifier we supply it with two filter-based feature selection strategies. The proposed approaches could be used for a phase-coded brain-computer interface, enabling to encode several targets using only one stimulation frequency. The proposed classifier is a multichannel one, which distinguishes our approach from the existing single-channel ones. We show that the proposed approach outperforms others in terms of accuracy and length of the data segments used for decoding. We show that the decoding based on one optimally selected channel yields an inferior performance compared to the one based on several features, which supports our argument for a multichannel approach.

Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling

The Mind Speller is a Brain-Computer Interface which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms. This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. We report on the implementation of a feature extraction procedure on a new ultra low-power 8-channel wireless EEG device. The feature extraction procedure is based on downsampled EEG signal epochs, the Students t-statistic of the Continuous Wavelet Transform, and the Common Spatial Pattern technique. For classification, we use a linear Least-Squares Support Vector Machine. The results show that subjects are potentially able to communicate a character in less than ten seconds with an accuracy of 94.5%, which is more than twice as fast as the state of the art. In addition since our EEG device is wireless it offers an increased comfort to the subject.

Synchronization in monkey visual cortex analyzed with an information-theoretic measure

We apply an information-theoretic measure for phase synchrony to local field potentials (LFPs) [corrected] recorded with a multi-electrode array implanted in area V4 of the monkey visual cortex. We show for the first time statistically significant stimulus-dependent synchrony of the visual cortical LFPs and this during different, short time intervals of the response. Furthermore, we could compute waves of synchronous activity over the array and correlate their timing with the stimulus-dependent difference in synchrony [corrected]

Language model applications to spelling with Brain-Computer Interfaces.

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.

Decoding Stimulus-Reward Pairing From Local Field Potentials Recorded From Monkey Visual Cortex

Single-trial decoding of brain recordings is a real challenge, since it pushes the signal-to-noise ratio issue to the limit. In this paper, we concentrate on the single-trial decoding of stimulus-reward pairing from local field potentials (LFPs) recorded chronically in the visual cortical area V4 of monkeys during a perceptual conditioning task. We developed a set of physiologically meaningful features that can classify and monitor the monkeys training performance. One of these features is based on the recently discovered propagation of waves of LFPs in the visual cortex. Time-frequency features together with spatial features (phase synchrony and wave propagation) yield, after applying a feature selection procedure, an exceptionally good single-trial classification performance, even when using a linear classifier.

DECODING GRATING ORIENTATION FROM MICROELECTRODE ARRAY RECORDINGS IN MONKEY CORTICAL AREA V4

We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the gratings spatial frequency. Since pyramidal cells are less prominent in visual areas, compared to (pre)motor areas, the recordings contain spikes with smaller amplitudes, compared to the noise level. Hence, rather than performing spike decoding, feature selection algorithms are applied to extract the required information for the decoder. Two types of feature selection procedures are compared, filter and wrapper. The wrapper is combined with a linear discriminant analysis classifier, and the filter is followed by a radial-basis function support vector machine classifier. In addition, since we have a multiclass classification problen, different methods for combining pairwise classifiers are compared.

Steady State Visual Evoked Potential Based Computer Gaming – The Maze

We introduce a game, called “The Maze”, as a brain-computer interface (BCI) application in which an avatar is navigated through a maze by analyzing the player’s steady-state visual evoked potential (SSVEP) responses recorded with electroencephalography (EEG). The same computer screen is used for displaying the game environment and for the visual stimulation. The algorithms for EEG data processing and SSVEP detection are discussed in depth. We propose the system parameter values, which provide an acceptable trade-off between the game control accuracy and interactivity.