集団同時脳波計測 : P300の単試行検出精度の向上に向けて(<特集>VR心理学5～脳機能計測とVR～)

抄録

Brain-Machine Interface (BMI) is a technology that can be used to interact with computers only by means of brain activities. Electroencephalogram (EEG) is used in many cases and the conventional BMI has been operated by individual subject with averaging brain signals. It has been required to improve the information transfer rate and to show new application concepts. In this paper, aiming to realize new BMI applications with improved information transfer rate, we focus on population EEG. The simultaneous measurements of P300 with three subjects were performed by using visual oddball paradigm, and the detection accuracy of population P300 was studied consequently with nine subjects. The machine learning was performed and it was found the accuracy with population subjects was remarkably higher than that with individual subject. This technique might be applied in future to the study in social psychology, neuromarketing in economics, life-log and CSCW in information systems engineering and entertainment etc.