Abstract
The Brain-Machine Interface (BMI) is an interface technology that utilizes neurophysiological signals from the brain to control external machines or computers. We have developed EEG-based BMI systems. We first applied the P300 paradigm for communication and environmental control. We prepared a green/blue flicker matrix, and showed that the new matrix was associated with a better subjective feeling of comfort than was the conventional white/gray flicker matrix, and we also found that the new matrix was associated with better performance (Takano, et al., 2009). We also prepared a steady-state visual evoked potential-based BMI using flickering visual stimuli at frequencies greater than the critical flicker frequency (Sakurada, et al., in press). For clinical purposes, we have developed an in-house environmental control system. We also developed peripheral devices: a non-adhesive solid-gel EEG electrode (Toyama, et al., 2012) and a soft cap with electrode holders. The BMI system was successfully operated by patients with amyotrophic lateral sclerosis (Ikegami, et al., 2014). Researches along these lines may help persons with disabilities to expand the range of activities.
