Bitte wählen Sie ihr Lieferland und ihre Kundengruppe
In the paper, the authors address the importance of feature selection for electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems operated by motor imagery (imagination of movements). The EEG of thirty-four healthy, naive subjects, while performing left and right hand motor imagery, was analyzed in respect to optimal (i) electrode location and (ii) reactive frequency components. The results show that in 56 % of the cases highest accuracies were achieved when classifying the EEG recorded from areas anterior to C3/C4. The mu (11 - 13 Hz) components were most relevant for classification in 79.4 % of the subjects.
Relevant electrode positions and frequency components for motor-imagery based brain-computer communications
Weitere Titelangaben:
Relevante Elektrodenpositionierung und Frequenzkomponenten für eine auf der Vorstellung von Bewegung (Imagination) basierenden Gehirn-Computer-Kommunikation