A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.

A neural network approach has been developed for detecting EEG patterns accompanying the implementation of motor imagery, which are mental equivalents of real movements. The method is based...

Spectral power (SP) ratios of the EEG α and β frequencies were used as neurofeedback (NFB) for ten apparently healthy subjects (university students) to control a computer cursor in a graphic interface in three scenarios. The results showed that the scenario that used the SP ratio of the α and β₂ frequencies provided for the highest accuracy and best speed of task performance. The ratios including the β₁ frequency were found to be less efficient because an increase or a decrease in its SP could occur together with an increase or a decrease of the SPs of the α and β₂ frequencies. However, the subjects acquired the skill of controlling the cursor with an efficiency of 81%, which gradually grew during learning, after a relatively short period of time (5 training sessions within 2 weeks).