EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.
EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.
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发表时间：
1995
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通讯作者：
Gert Pfurtscheller;D. Flotzinger;M. Pregenzer;Wolpaw;Dennis J. McFarland
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作者：
Gert Pfurtscheller;D. Flotzinger;M. Pregenzer;Wolpaw;Dennis J. McFarland
世界各地的几个实验室最近开始研究基于脑电图的脑机接口（BCI）系统，以便为患有严重运动障碍的受试者创建新的沟通渠道。本文描述了对记录的 64 通道 EEG 数据的初步评估，同时受试者使用左侧感觉运动区域的一个 EEG 通道来控制在线垂直光标移动。目标在计算机屏幕的顶部或底部给出。通过分别计算顶部和底部目标的频带功率时间过程和图来分析 3 个受试者在训练早期阶段的数据。此外，将区分敏感学习矢量量化器（DSLVQ）应用于单次试验脑电图数据。研究发现，对于每个受试者，都存在用于基于脑电图的在线光标控制的最佳电极位置和频率分量。
Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.