Pavel Bobrov

Brain-Computer Interface Based on Generation of Visual Images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.

Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton

The neurophysiological prerequisites for the development and operation of the brain-computer interfaces (BCI) that allow cerebral electrical signals alone to control external technical devices are considered. A BCI based on the discrimination of the EEG patterns related to imagery of extremity movements is described. The possibility of the rehabilitation of patients with motor disorders by means of the BCI based on motor imagery and the exoskeleton controlled by it is discussed.

Motor imagery and its practical application

The physiological mechanisms underlying the process of motor imagery have significant similarities with the mechanisms of motor control, and this can be used for the rehabilitation of patients with movement disorders. In patients with severe paresis, motor imagery may be the only method producing movement recovery. Over the last decade, this has led to increasing interest in studies of the function of motor imagery. Brain–computer interface technologies can be used monitor training with imaginary movements.

SOURCES OF EEG ACTIVITY MOST RELEVANT TO PERFORMANCE OF BRAIN-COMPUTER INTERFACE BASED ON MOTOR IMAGERY

The paper examines sources of brain activity, contributing to EEG pat- terns which correspond to motor imagery during training to control brain-computer interface. To identify individual source contribution into electroencephalogram recorded during the training Independent Component Analysis was used. Then those independent components for which the BCI system classification accuracy was at maximum were treated as relevant to performing the motor imagery tasks, since they demonstrated well exposed event related de-synchronization and event related synchronization of the sensorimotor -rhythm during imagining of contra- and ipsilateral hand movements. To reveal neurophysiological nature of these com- ponents we have solved the inverse EEG problem to locate the sources of brain activity causing these components to appear in EEG. The sources were located in hand representation areas of the primary sensorimotor cortex. Their positions practically coincide with the regions of brain activity during the motor imagination obtained in fMRI study. Individual geometry of brain and its covers provided by anatomical MR images was taken into account when localizing the sources.

Localization of brain electrical activity sources and hemodynamic activity foci during motor imagery

The sources of brain activity that make the maximum contribution to EEG patterns corresponding to motor imagery have been studied. The accuracy of their classification determines the efficiency of brain-computer interface (BCI) for controlling external technical devices directly by brain signals, without the involvement of muscle activity. Brain activity sources are identified by independent component analysis. The independent components providing the maximum BCI classification accuracy are considered relevant for the motor imagery task. The two most relevant sources exhibit clearly marked event-related desynchronization and synchronization of the μ-rhythm during the imagery of contra- and ipsilateral hand movements. These sources were localized by solving the inverse EEG problem with due consideration for individual geometry of the brain and its covers, as determined by magnetic resonance imaging. Each of the sources was shown to be localized in the 3a area of the primary somatosensory cortex corresponding to proprioceptive sensitivity of the contralateral hand. Their positions were close to the foci of BOLD activity obtained by fMRI.

Rehabilitation of Stroke Patients with a Bioengineered “Brain–Computer Interface with Exoskeleton” System

Objective. To study the potential for use of a bioengineered system consisting of an electroencephalograph, a personal computer running a program for the synchronous data transmission, recognition, and classification of electroencephalogram (EEG) signals, and formation of control commands in real time, combined with a hand exoskeleton (a bioengineered “brain–computer interface (BCI) with exoskeleton” system) for the motor rehabilitation of patients with poststroke upper limb paresis. Materials and methods. Brain–computer interfaces have potential for use in neurorehabilitation. A total of five patients with poststroke upper limb paresis received neurorehabilitation courses consisting of 8–10 sessions. All the patients had large foci of poststroke changes of cortical-subcortical locations as demonstrated by MRI scans. Results. Improvements in neurological status on the NIHSS were seen after courses of sessions, with significant increases in the volume and strength of movements in the paralyzed hand, improvements in the coordination of its movements, and minor decreases in the level of spasticity. There was an increase in daily activity on the Barthel index, mainly due to improvement in fi ne motor function. Levels of disability showed clear changes on the modified Rankin scale. Conclusions. Use of the “brain–computer interface (BCI) with exoskeleton” system in the rehabilitation of patients with poststroke paresis of the hand gave positive results, pointing to the need to continue these studies.

Recovery of the motor function of the arm with the aid of a hand exoskeleton controlled by a brain–computer interface in a patient with an extensive brain lesion

The dynamics of motor function recovery in a patient with an extensive brain lesion has been investigated during a course of neurorehabilitation assisted by a hand exoskeleton controlled by a brain–computer interface. Biomechanical analysis of the movements of the paretic arm recorded during the rehabilitation course was used for an unbiased assessment of motor function. Fifteen procedures involving hand exoskeleton control (one procedure per week) yielded the following results: (a) the velocity profile for targeted movements of the paretic hand became nearly bell-shaped; (b) the patient began to extend and abduct the hand, which was flexed and adducted at the beginning of the course; and (c) the patient started supinating the forearm, which was pronated at the beginning of the rehabilitation course. The first result is interpreted as improvement of the general level of control over the paretic hand, and the two other results are interpreted as a decrease in spasticity of the paretic hand.

Sources of Electrical Brain Activity Most Relevant to Performance of Brain-Computer Interface Based on Motor Imagery

A brain-computer interface (BCI) provides a direct functional interaction between the human brain and the external device. Many kinds of signals (from electromagnetic to metabolic [23, 38, 42]) could be used in BCI. However the most widespread BCI systems are based on EEG recordings. BCI consists of a brain signal acquisition system, data processing software for feature extraction and pattern classification, and a system to transfer commands to an external device and, thus, providing feedback to an operator. The most prevalent BCI systems are based on the discrimination of EEG patterns related to execution of different mental tasks [14, 21, 24]. This approach is justified by the presence of correlation between brain signal features and tasks performed, revealed by basic research [24, 28, 30, 45]. By agreement with the BCI operator each mental task is associated with one of the commands to the external device. Then to produce the commands, the operator switches voluntary between corresponding mental tasks. If BCI is dedicated to control device movements then psychologically convenient mental tasks are motor imaginations. For example, when a patient controls by BCI the movement of a wheelchair its movement to the left can be associated with the imagination of the left arm movement and movement to the right with right arm movement. Another advantage of these mental tasks is that their performance is accompanied by the easily recognizable EEG patterns. Moreover, motor imagination is considered now as an efficient rehabilitation procedure to restore movement after paralysis [4]. Thus, namely the analysis of BCI performance based on motor imagination is the object of the present chapter.

Brain-Computer Interface: Common Tensor Discriminant Analysis classifier evaluation

The performance of the Common Tensor Discriminant Analysis method for Brain-Computer Interface EEG pattern classification is compared with three other classifiers. The classifiers are designed with the aim to distinguish EEG patterns appearing as a result of performance of several mental tasks. Classifier comparison has yielded quite similar results as regards our experimental imagery movement data set as well as for BCI Competition IV data set. The Bayesian and Multiclass Common Spatial Patterns classifiers, which use solely interchannel covariance as input, are shown to be comparable in performance, while lagging behind the Multiclass Common Spatial Patterns classifier and the Common Tensor Discriminant Analysis classifier, that is classifiers which additionally account for EEG frequency structure. It is shown that the Common Tensor Discriminant Analysis classifier and the Multiclass Common Spatial Patterns classifier provide significantly better classification than other two methods but at a higher computational cost.

Sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain–Computer interface based on classification of EEG patterns and near-infrared spectrography signals during motor imagery

A method is described for joint use of electroencephalography and near-infrared spectrography to locate sources of electrophysiological and foci of hemodynamic brain activity during motor execution and imagination. The sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain-computer interface based on motor imagery are revealed and discussed.