Petar Horki

An adaptive P300-based control system

An adaptive P300 brain-computer interface (BCI) using a 12 × 7 matrix explored new paradigms to improve bit rate and accuracy. During online use, the system adaptively selects the number of flashes to average. Five different flash patterns were tested. The 19-flash paradigm represents the typical row/column presentation (i.e. 12 columns and 7 rows). The 9- and 14-flash A and B paradigms present all items of the 12 × 7 matrix three times using either 9 or 14 flashes (instead of 19), decreasing the amount of time to present stimuli. Compared to 9-flash A, 9-flash B decreased the likelihood that neighboring items would flash when the target was not flashing, thereby reducing the interference from items adjacent to targets. 14-flash A also reduced the adjacent item interference and 14-flash B additionally eliminated successive (double) flashes of the same item. Results showed that the accuracy and bit rate of the adaptive system were higher than those of the non-adaptive system. In addition, 9- and 14-flash B produced significantly higher performance than their respective A conditions. The results also show the trend that the 14-flash B paradigm was better than the 19-flash pattern for naive users.

Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb

A Brain–Computer Interface (BCI) is a device that transforms brain signals, which are intentionally modulated by a user, into control commands. BCIs based on motor imagery (MI) and steady-state visual evoked potentials (SSVEP) can partially restore motor control in spinal cord injured patients. To determine whether these BCIs can be combined for grasp and elbow function control independently, we investigated a control method where the beta rebound after brisk feet MI is used to control the grasp function, and a two-class SSVEP-BCI the elbow function of a 2 degrees-of-freedom artificial upper limb. Subjective preferences for the BCI control were assessed with a questionnaire. The results of the initial evaluation of the system suggests that this is feasible.

A SINGLE-SWITCH BCI BASED ON PASSIVE AND IMAGINED MOVEMENTS: TOWARD RESTORING COMMUNICATION IN MINIMALLY CONSCIOUS PATIENTS

We investigate whether an electroencephalography technique could be used for yes/no communication with auditory scanning. To be usable by the target group, i.e., minimally conscious individuals, such a brain-computer interface (BCI) has to be very simple and robust. This leads to the concept of a single-switch BCI (ssBCI). With an ssBCI it is possible to reliably detect one certain, individually trained, brain pattern of the individual, and use it to control all kinds of applications using yes/no responses. A total of 10 healthy volunteers (20-27 years) participated in an initial cue-based session with a motor imagery (MI) task after brisk passive feet/hand movement. Four of them reached MI classification accuracies above 70% and, thus, fulfilled the inclusion criterion for participation in the 2nd session. In the 2nd session, MI was used to communicate yes/no answers to a series of questions in an auditory scanning mode. Two of the three participants of the 2nd session were able to reliably communicate their intent with 90% or above correct and 0% false responses. This work showed, for the 1st time, the use of a ssBCI based on passive and imagined movements for communication in auditory scanning mode.

A new P300 stimulus presentation pattern for EEG-based spelling systems

Abstract A P300 spelling system is one of the most popular EEG-based spelling systems. This system is normally presented as a matrix and allows its users to select one of many options by focused attention. It is possible to use large matrices as a large menu (computer keyboard, etc.), but then more time is required for each selection, because all rows and columns of the matrix must flash once per trial to locate the target character in the row/column (RC) speller method. In this paper, a new flash pattern design based on mathematical combinations is suggested. This new method decreases the number of flashes required in each trial. A typical example of a 6×6 matrix is considered. Only 9 flashes per trial for the 6×6 matrix are required in this new method, which is 3 flashes less than the RC speller method (12 flashes per trial). In this paper, practical bit rate was used. Results from offline analysis have shown that the 9-flash pattern yielded significantly higher practical bit rate than the 12-flash pattern (RC pattern).

Asynchronous steady-state visual evoked potential based BCI control of a 2-DoF artificial upper limb.

Abstract A brain-computer interface (BCI) provides a direct connection between the human brain and a computer. One type of BCI can be realized using steady-state visual evoked potentials (SSVEPs), resulting from repetitive stimulation. The aim of this study was the realization of an asynchronous SSVEP-BCI, based on canonical correlation analysis, suitable for the control of a 2-degrees of freedom (DoF) hand and elbow neuroprosthesis. To determine whether this BCI is suitable for the control of 2-DoF neuroprosthetic devices, online experiments with a virtual and a robotic limb feedback were conducted with eight healthy subjects and one tetraplegic patient. All participants were able to control the artificial limbs with the BCI. In the online experiments, the positive predictive value (PPV) varied between 69% and 83% and the false negative rate (FNR) varied between 1% and 17%. The spinal cord injured patient achieved PPV and FNR values within one standard deviation of the mean for all healthy subjects.

Detection of mental imagery and attempted movements in patients with disorders of consciousness using EEG

Further development of an EEG based communication device for patients with disorders of consciousness (DoC) could benefit from addressing the following gaps in knowledge—first, an evaluation of different types of motor imagery; second, an evaluation of passive feet movement as a mean of an initial classifier setup; and third, rapid delivery of biased feedback. To that end we investigated whether complex and/or familiar mental imagery, passive, and attempted feet movement can be reliably detected in patients with DoC using EEG recordings, aiming to provide them with a means of communication. Six patients in a minimally conscious state (MCS) took part in this study. The patients were verbally instructed to perform different mental imagery tasks (sport, navigation), as well as attempted feet movements, to induce distinctive event-related (de)synchronization (ERD/S) patterns in the EEG. Offline classification accuracies above chance level were reached in all three tasks (i.e., attempted feet, sport, and navigation), with motor tasks yielding significant (p < 0.05) results more often than navigation (sport: 10 out of 18 sessions; attempted feet: 7 out of 14 sessions; navigation: 4 out of 12 sessions). The passive feet movements, evaluated in one patient, yielded mixed results: whereas time-frequency analysis revealed task-related EEG changes over neurophysiological plausible cortical areas, the classification results were not significant enough (p < 0.05) to setup an initial classifier for the detection of attempted movements. Concluding, the results presented in this study are consistent with the current state of the art in similar studies, to which we contributed by comparing different types of mental tasks, notably complex motor imagery and attempted feet movements, within patients. Furthermore, we explored new venues, such as an evaluation of passive feet movement as a mean of an initial classifier setup, and rapid delivery of biased feedback.

Principles of Hybrid Brain–Computer Interfaces

Brain–Computer Interface (BCI) research has developed in the last decade so that BCIs are ready to be used with users outside the research labs. Although a wide range of assistive devices (ADs) exist, the additional usage of a BCI could improve the overall performance or applicability of such a combined system and is called hybrid BCI (hBCI). In this chapter the development of hBCIs starting from specific BCI combinations to very general hBCI based on EEG, biosignals and ADs is presented.

Evaluation of Healthy EEG Responses for Spelling Through Listener-Assisted Scanning

We investigated whether listener-assisted scanning, an alternative communication method for persons with severe motor and visual impairments but preserved cognitive skills, could be used for spelling with EEG. To that end spoken letters were presented sequentially, and the participants made selections by performing motor execution/imagery or a cognitive task. The motor task was a brisk dorsiflexion of both feet, and the cognitive task was related to working memory and perception of human voice. The motor imagery task yielded the most promising results with respect to letter selection accuracy, albeit with a large variation in individual performance. The cognitive task yielded significant (p = 0.05) albeit moderate results. Closer inspection of grand average ERPs for the cognitive task revealed task-related modulation of a late negative component, which is novel in the auditory BCI literature. Guidelines for further development are presented.

Hilbert-Huang Time-Frequency Analysis of Motor Imagery EEG Data for Brain-Computer Interfaces

Brain-computer interface (BCI) is a technology that provides a non-muscular communication channel between a brain and the outside world. Imagination of left and right hand movements results in spatially distinct brain activation patterns that can be used as control signals for the BCI. Motor imagery (MI) results in the attenuation (event related desynchronization, ERD) or enhancement (event related synchronization, ERS) of amplitude in a certain frequency band of electroencephalogram (EEG). This frequency band can vary between different participants. Therefore time-frequency (TF) analysis is performed in order to extract interesting features from EEG. A simple way of performing TF analysis is by using band power features. In this paper we investigate the perspective of Hilbert-Huang transform (HHT) for extracting TF information used for MI classification. HHT is a method that allows calculation of instantaneous frequency and amplitude of the signal. It does that by decomposing the signal into components for which these parameters can be calculated by means of Hilbert transform. We compare classification accuracy of simple band power features and features obtained by means of HHT on BCI competition IV dataset 2b.

Improved concept and first results of an auditory single-switch BCI for the future use in disorders of consciousness patients

A promising approach to establish basic communication for disorders of consciousness (DOC) patients, is the application of Brain-Computer Interface (BCI) systems, especially the use of single-switch BCIs (ssBCIs). Recently we proposed the concept of a novel auditory ssBCI paradigm and presented first classification results. In this study we report on the evaluation of four different modifications of the original paradigm with the intention to increase the suitability. Therefore we investigated different sound types and the inclusion of additional spatial information. Finally, the classification investigation with the most encouraging modifications shows an enhancement compared to our original paradigm, within healthy subjects, implicating better results for the future use in DOC patients.