Single-trial EEG Discrimination between Wrist and Finger Movement
  Imagery and Execution in a Sensorimotor BCI
  A brain-computer interface (BCI) may be used to control a prosthetic or
orthotic hand using neural activity from the brain. The core of this
sensorimotor BCI lies in the interpretation of the neural information extracted
from electroencephalogram (EEG). It is desired to improve on the interpretation
of EEG to allow people with neuromuscular disorders to perform daily
activities. This paper investigates the possibility of discriminating between
the EEG associated with wrist and finger movements. The EEG was recorded from
test subjects as they executed and imagined five essential hand movements using
both hands. Independent component analysis (ICA) and time-frequency techniques
were used to extract spectral features based on event-related
(de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used
for feature reduction. Mahalanobis distance (MD) clustering and artificial
neural networks (ANN) were used as classifiers and obtained average accuracies
of 65 % and 71 % respectively. This shows that EEG discrimination between wrist
and finger movements is possible. The research introduces a new combination of
motor tasks to BCI research.
