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 frequencydomain
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.