Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects

Background: Motor imagery (MI) is the mental performance of movement without muscle activity. It is generally accepted that MI and motor performance have similar physiological mechanisms. Purpose: To investigate the activity and excitability of cortical motor areas during MI in subjects who were previously trained with an MI-based brain-computer interface (BCI). Subjects and Methods: Eleven healthy volunteers without neurological impairments (mean age, 36 years; range: 24–68 years) were either trained with an MI-based BCI (BCI-trained, n = 5) or received no BCI training (n = 6, controls). Subjects imagined grasping in a blocked paradigm task with alternating rest and task periods. For evaluating the activity and excitability of cortical motor areas we used functional MRI and navigated transcranial magnetic stimulation (nTMS). Results: fMRI revealed activation in Brodmann areas 3 and 6, the cerebellum, and the thalamus during MI in all subjects. The primary motor cortex was activated only in BCI-trained subjects. The associative zones of activation were larger in non-trained subjects. During MI, motor evoked potentials recorded from two of the three targeted muscles were significantly higher only in BCI-trained subjects. The motor threshold decreased (median = 17%) during MI, which was also observed only in BCI-trained subjects. Conclusion: Previous BCI training increased motor cortex excitability during MI. These data may help to improve BCI applications, including rehabilitation of patients with cerebral palsy.Web of Science7art. no. 0016

Comparison of four classification methods for brain-computer interface

This paper examines the performance of four classifiers for Brain Computer Interface (BCI) systems based on multichannel EEG recordings. The classifiers are designed to distinguish EEG patterns corresponding to performance of several mental tasks. The first one is the basic Bayesian classifier (BC) which exploits only interchannel covariance matrices corresponding to different mental tasks. The second classifier is also based on Bayesian approach but it takes into account EEG frequency structure by exploiting interchannel covariance matrices estimated separately for several frequency bands (Multiband Bayesian Classifier, MBBC). The third one is based on the method of Multiclass Common Spatial Patterns (MSCP) exploiting only interchannel covariance matrices as BC. The fourth one is based on the Common Tensor Discriminant Analysis (CTDA), which is a generalization of MCSP, taking EEG frequency structure into account. The MBBC and CTDA classifiers are shown to perform significantly better than the two other methods. Computational complexity of the four methods is estimated. It is shown that for all classifiers the increase in the classifying quality is always accompanied by a significant increase of computational complexity.Web of Science21211510

Bajesovskij podchod k realizacii interferjsa mozg-komp’juter, osnovannogo na predstavlenii dviženij

V stat'e rassmatrivaetsya ispol'zovanie baiesovskogo podkhoda k sozdaniyu klassifikatora dlya interfeisa mozg-komp'yuter, osnovannogo na raspoznavanii patternov EEG pri voobrazhenii fiksirovannogo nabora dvizhenii razlichnymi konechnostyami. Pokazano, chto prosteishii baiesovskii klassifikator, osnovannyi neposredstvenno na analize kovariatsionnykh matrits iskhodnogo signala mnogokanal'nykh zapisei EEG, ne ustupaet po effektivnosti klassifikatoru, osnovannomu na metode MCSP (Multiclass Common Spatial Patterns), obespechivayushchemu, po dannym literatury, nailuchshee raspoznavanie patternov EEG v interfeisakh mozg-komp'yuter. Issledovano vliyanie artefaktov, svyazannykh s dvizheniem glaz i morganiem, na kachestvo klassifikatsii patternov EEG i pokazano, chto ikh nalichie ne vliyaet na kachestvo raspoznavaniya.В статье рассматривается использование байесовского подхода к созданию классификатора для интерфейса мозг-компьютер, основанного на распознавании паттернов ЭЭГ при воображении фиксированного набора движений различными конечностями. Показано, что простейший байесовский классификатор, основанный непосредственно на анализе ковариационных матриц исходного сигнала многоканальных записей ЭЭГ, не уступает по эффективности классификатору, основанному на методе MCSP (Multiclass Common Spatial Patterns), обеспечивающему, по данным литературы, наилучшее распознавание паттернов ЭЭГ в интерфейсах мозг-компьютер. Исследовано влияние артефактов, связанных с движением глаз и морганием, на качество классификации паттернов ЭЭГ и показано, что их наличие не влияет на качество распознавания.Web of Science621998

Voobraženije dviženija i jego praktičeskoje primenenije

The mechanisms underlying the process of motor imagery are similar to the motor control mechanisms. It can be used for motor learning in patients with movement disorders. Motor imagery may be the only one method for recovery of motor function in patients with severe paresis. It was the prerequisite of increased scientist interest in motor imagery during last decade. Brain-computer interface technology can support the motor imagery trainings.Web of Science63220419

Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery

Motor recovery in post-stroke and post-traumatic patients using exoskeleton controlled by the brain-computer interface (BCI) is a new and promising rehabilitation procedure. Its development is a multidisciplinary research which requires, the teamwork of experts in neurology, neurophysiology, physics, mathematics, biomechanics and robotics. Some aspects of all these fields of study concerning the development of this rehabilitation procedure are described in the paper. The description includes the principles and physiological prerequisites of BCI based on motor imagery, biologically adequate principles of exoskeleton design and control and the results of clinical application.Web of Science27113710

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 patterns 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 μ-rhythm during imagining of contra- and ipsilateral hand movements. To reveal neurophysiological nature of these components 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.Web of Science221372