Brain–Machine Interfaces for Persons with Disabilities
The brain–machine interface (BMI) or brain–computer interface (BCI) is a new interface technology that utilizes neurophysiological signals from the brain to control external machines or computers. We used electroencephalography signals in a BMI system that enables environmental control and communication using the P300 paradigm, which presents a selection of icons arranged in a matrix. The subject focuses attention on one of the flickering icons in the matrix as a target. We also prepared a green/blue flicker matrix because this color combination is considered the safest chromatic combination for patients with photosensitive epilepsy. We showed that the green/blue flicker matrix was associated with a better subjective feeling of comfort than was the white/gray flicker matrix, and we also found that the green/blue flicker matrix was associated with better performance. We further added augmented reality (AR) to make an AR-BMI system, in which the user’s brain signals controlled an agent robot and operated devices in the robot’s environment. Thus, the user’s thoughts became reality through the robot’s eyes, enabling the augmentation of real environments outside of the human body. Studies along these lines may provide useful information to expand the range of activities in persons with disabilities.
KeywordsAugmented Reality Motor Imagery Task Agent Robot Offline Evaluation P300 Speller
These studies were conducted with the NRCD post-doctoral fellows Drs. Kouji Takano, Tomoaki Komatsu, Shiro Ikegami, and Naoki Hata. I thank Dr. Günter Edlinger for his help, and Drs. Yasoichi Nakajima and Motoi Suwa for their continuous encouragement.
- 14.Komatsu T, Hata N, Nakajima Y, Kansaku K (2008) A non-training EEG-based BMI system for environmental control. Neurosci Res Suppl 61:S251Google Scholar
- 15.Ikegami S, Takano K, Komatsu T, Saeki N, Kansaku K (2009) Operation of a BMI based environmental control system by patients with cervical spinal cord injury. Neuroscience meeting planner. Society for Neuroscience, Chicago. Online Program No. 664.16Google Scholar
- 19.Takano K, Hata N, Nakajima Y, Kansaku K (2010) AR-BMI operated with a HMD: effects of channel selection. Neuroscience meeting planner. Society for Neuroscience, San Diego. Online Program No. 295.18Google Scholar
- 24.Krusienski DJ, Sellers EW, Vaughan TM (2007) Common spatio-temporal patterns for the p300 speller. In: 3rd International IEEE EMBS conference on neural engineering, Kohala Coast, Hawaii, USA, pp 421–424Google Scholar
- 25.Lu S, Guan C, Zhang H (2008) Unsupervised brain computer interface based on inter-subject information. In: 30th Annual international IEEE EMBS conference, Vancouver, British Columbia, Canada, pp 638–641Google Scholar
- 27.Kato H, Billinghurst M (1999) Marker tracking and HMD calibration for a video-based augmented reality conferencing system. In: International workshop on augmented reality, San Francisco, USA, pp 85–94Google Scholar
- 32.Komatsu T, Takano K, Nakajima Y, Kansaku K (2009) A BMI based environmental control system: a combination of sensorimotor rhythm, P300, and virtual reality. Neuroscience meeting planner. Society for Neuroscience, Chicago. Online Program No. 360.14Google Scholar
- 33.Komatsu T, Takano K, Ikegami S, Kansaku K (2010) A development of a BCI-based OT-assist suit for paralyzed upper extremities. Neuroscience meeting planner. Society for Neuroscience, San Diego. Online Program No. 295.9Google Scholar