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A Wavelet-CSP method to classify hand movement directions in EEG based BCI system

机译:基于脑电图的BCI系统中的小波CSP方法对手的运动方向进行分类

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The Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a non-invasive system to acquire, decode and convert brain signals into control signals for an external device. The Motor-Imagery based BCI (MI-BCI) efficiently decodes the brain signals from the imagination of movement but the performance is limited by the number of commands such as right and left hand motor imageries. However, other parameters of an actual voluntary movement, such as the direction of movement, speed and extent, are encoded in the brain signals. This paper investigates the EEG brain signals from directional changes in actual hand movement. The Wavelet-Common Spatial Pattern algorithm is proposed to extract discriminative features of the brain signals that carries the direction-related information. The experiment performed on two subjects yielded a mean classification accuracy of 87.85% in decoding two classes of the direction-related information.
机译:基于脑电图(EEG)的脑计算机接口(BCI)是一种非侵入性系统,用于获取,解码脑信号并将其转换为外部设备的控制信号。基于Motor-Imagery的BCI(MI-BCI)可有效地根据运动想象来解码大脑信号,但其性能受到命令数量(如左右手运动图像)的限制。但是,实际自愿运动的其他参数(例如运动方向,速度和范围)会编码在大脑信号中。本文研究了来自实际手部运动方向变化的脑电图脑信号。提出了小波公共空间模式算法,以提取携带方向相关信息的脑信号的判别特征。对两个主题进行的实验在解码两类方向相关信息时产生了87.85%的平均分类精度。

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