Çoklu-değişkenli deneysel kip ayrıştırımı-tabanlı işlevsel bağlantısallık öznitellikleriyle motor imgeleme sınıflandırması

Electroencephalogram (EEG) motor imagery signals are widely used for the implementation of brain-computer interfaces (BCI). Recently, functional connectivity measures have attracted attention as they can be used to capture statistical dependencies among EEG channels. However, functional connectivity during motor imagery tasks have not been fully explored. This study utilizes Instrinsic Mode Function (IMF) level phase locking value (PLV), coherence, and imaginary part of coherency in lefthand/right-hand motor imagery classification. EEG signals are decomposed into IMFs via noise-assisted multidimensional empirical mode decomposition (NA-MEMD), and connectivity metrics over the selected four channels are calculated for each trial as raw data and as a function of time and frequency. Resulting features are used to train multiple classifiers and their accuracy scores are analyzed. Best results are obtained using features derived from time-frequency functions of imaginary part of frequency where the overall accuracy score of 0.77 is achieved. This study shows that the change of connectivity throughout the duration of the task provides a more effective feature than connectivity calculated using each trial as raw data. Achieved accuracy scores are comparable to similar studies with the additional advantage of using few number of channels.