Combining Frequency And Time-Domain Eeg Features For Classification Of Self-Paced Reach-And-Grasp Actions

Brain-computer interfaces (BCIs) might provide an intuitive way for severely motor impaired persons to operate assistive devices to perform daily life activities. Recent studies have shown that complex hand movements, such as reachand-grasp tasks, can be decoded from the low frequency of the electroencephalogram (EEG). In this work we investigated whether additional features extracted from the frequency-domain of alpha and beta bands could improve classification performance of rest vs. palmar vs. lateral grasp. We analysed two multi-class classification approaches, the first using features from the low frequency time-domain, and the second in which we combined the time-domain with frequency-domain features from alpha and beta bands. We measured EEG of ten participants without motor disability which performed self-paced reach-and-grasp actions on objects of daily life. For the timedomain classification approach, participants reached an average peak accuracy of 65 %. For the combined approach, an average peak accuracy of 75 % was reached. In both approaches and for all subjects, performance was significantly higher than chance level (38.1%, 3-class scenario). By computing the confusion matrices as well as feature rankings through the Fisher score, we show that movement vs. rest classification performance increased considerably in the combined approach and was the main responsible for the multi-class higher performance. These findings could help the development of BCIs in real-life scenarios, where decreasing false movement detections could drastically increase the end-user acceptance and usability of BCIs.