  There have been different reports of developing Brain-Computer Interface
(BCI) platforms to investigate the noninvasive electroencephalography (EEG)
signals associated with plan-to-grasp tasks in humans. However, these reports
were unable to clearly show evidence of emerging neural activity from the
planning (observation) phase - dominated by the vision cortices - to grasp
execution - dominated by the motor cortices. In this study, we developed a
novel vision-based grasping BCI platform that distinguishes different grip
types (power and precision) through the phases of plan-to-grasp tasks using EEG
signals. Using our platform and extracting features from Filter Bank Common
Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains
discriminative spatial patterns present in both the observation and movement
phases. Support Vector Machine (SVM) classification (power vs precision)
yielded high accuracy percentages of 74% and 68% for the observation and
movement phases in the alpha band, respectively.
