Non-Invasive BCI for the Decoding of Intended Arm Reaching Movement in Prosthetic Limb Control

Abstract

Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide an alternative means of communication with and control over external assistive devices. In general, EEG is insufficient to obtain detailed information about many degrees of freedom (DOF) for arm movements. The main objectives are to design a non-invasive BCI and create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects’ visual fixation to the target locations would have little or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) classifier to perform single-trial classification of the EEG to decode the intended arm movement in the left, right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310ms after visual stimulation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to improve the classification accuracy from 60.11% to 93.91% in the binary class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imagined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control.