An alternative human interface enabling the handicapped with severe motor disabilities to control an assistive system is presented. Since this interface relies on the biosignals originating from the contraction of muscles on the face during particular movements, even individuals with a paralyzed limb can use it with ease. For real-world application, a dedicated hardware module employing a general-purpose DSP was implemented and its validity tested on an electrically powered wheelchair. Furthermore, an additional attempt to reduce error rates to a minimum for stable operation was also made based on the entropy information inherent in the signals during the classification phase. In the experiments in which 11 subjects participated, it was found most of them could control the target system at their own will, and thus the proposed interface could be considered a potential alternative for the interaction of the severely handicapped with electronic systems.

We review the role of optics in interconnects, analog processing, neural networks, and digital computing. The properties of low interference, massively parallel interconnections, and very high data rates promise extremely high performance for optical information processing systems.

We review the role of optics in interconnects, analog processing, neural networks, and digital computing. The properties of low interference, massively parallel interconnections, and very high data rates promise extremely high performance for optical information processing systems.

An analog neurochip for independent component analysis (ICA) is designed with on-line learning capability. Due to the limited dynamic range of analog device, the nonholonomic ICA algorithm is adopted. In order to accommodate the offsets due to device mismatches, a modified algorithm is developed with 2-quadrant multipliers and self-adjusting biases. Performance of the developed system was demonstrated by Monte-Carlo simulation.