1. Bashashati, H., Ward, R. K., Birch, G. E., and Bashashati, A., “Comparing different classifiers in sensory motor brain computer interfaces,” PLoS One, 10, No. 6, e0129435 (2015).
2. Belda-Lois, J.-M., Mena-del Horno, S., Bermejo-Bosch, I., et al., “Rehabilitation of gait after stroke: a review towards a top-down approach,” J. Neuroeng. Rehabil., 8, 66 (2011).
3. Bobrov, P. D., Isaev, M. R., Korshakov, A. V., et al., “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,” Human Physiol., 42, No. 3, 241–251 (2016).
4. Bobrova, E. V., Frolov, A. A., and Reshetnikova, V. V., “Method sand approaches for optimizing control of a brain–computer interface system by healthy users and patients with motor impairments,” Zh. Vyssh. Nerv. Deyat.., 67, No. 4, 377–393 (2017a).
5. Bobrova, E. V., Reshetnikova, V. V., Volkova, K. V., and Frolov, A. A., “Effects of emotional stability on the success of learning to control a brain–computer interface system,” Zh. Vyssh. Nerv. Deyat., 67, No. 4, 485–492 (2017b).