Development of Brain wave controlled wheelchair by BSI-TOYOTA Collaboration Center
The BSI-TOYOTA Collaboration Center otherwise known as BTCC has successfully developing a system utilizing the fastest technology in the world in controlling a wheelchair using brain waves in a time as little as 125 milliseconds. Being a new signal processing technology for brain machine interface (BMI) application, it is a Real-time control of wheelchairs with brain waves. The Brian Machine Interface (BMI) based system would allow elderly or physically challenged individuals to interact with the world through signals from their brain without any needs to give voice commands. The recently developed technology uses combination of RIKEN’s blind signal separation and space-time-frequency filtering technologies to allow brain-wave analysis in as little as 125 ms in comparison to the conventional methods of several seconds.
The blind signal separation technology is capable of separating noise components and useful signal components from brain signals that can be used to control the wheelchair. It uses only one-line-recorded EEG signals, where as the Space-time-frequency filtering is a technology capable of extracting space and time patterns and frequency oscillation data from EEG electrodes to discriminate significant features and components which are able to reliably control the wheelchair. The system has the capability of adjusting to the characteristics of each individual driver resulting in improved efficiency in sensing driver’s command where an accuracy rate of 95% was achieved, one of the highest in the world. BTCC is planning to use this technology in a wide range of applications such as medical and nursing care management. Up to now the research has been concentrating on brain waves related to imaginary hand and foot control. However, this system may be applied to other types of brain waves generated by various mental states and emotions. Further R&D under consideration includes increasing the number of commands given and developing more efficient dry electrodes. The technology can also be applied in controlling robots in performing house chores for elderly.
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