J1801-1-6 fNIRSデータとネオコグニトロン型動画像識別モデルによるブレイン-コンピュータ・インタフェース(生体計測・ヒューマンファクタ)

概要

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We describe classification of functional near-infrared spectroscopy (fNIRS) data acquired during finger tapping and motor imagery tasks performed by a human subject, using an artificial neural network for image sequence recognition. Our goal is to develop a brain computer interface. We used an fNIRS system to collect neural information from brain activity. For discrimination of fNIRS data, we used our previously proposed neural network model called the Neocognitron-type Image Sequence Recognition Model (Neo-ISRM), which is suitable for multichannel temporal patterns. Finger tapping and its motor imagery were used as the motion and mental tasks to be discriminated using Neo-ISRM. The model gave good discrimination results for each category of tasks.
2009-09-12