Round Cosine Transform Based Feature Extraction of Motor Imagery EEG Signals
Round Cosine Transform Based Feature Extraction of Motor Imagery EEG Signals
复制标题
基于圆余弦变换的运动想象脑电信号特征提取
DOI：
10.1007/978-981-10-9038-7_94
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
T. Becker
中科院分区：
文献类型：
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作者：
R. Braga;C. Lopes;T. Becker
脑机接口（BCI）是一种具有巨大潜力的系统，可用于严重运动损伤患者的康复。通过分析受试者的脑电波，可以检测模式并将其“思维”转化为设备命令，如假肢或机器人手臂。本研究提出了一种EEG处理方法，该方法能够检测受试者的运动想象模式，将模式分为左手或右手想象。所提出的方法认为，圆余弦变换（RCT），一个低计算复杂度的变换，和一个人工神经网络（ANN）模块，识别的模式。该方法已在实时（RT）连续EEG处理实验模拟测试，控制鼠标箭头水平在屏幕上的受试者的图像运动活动的基础上。该方法的性能进行评估的互信息（MI），分类时间和错误分类率（%）。获得的结果分别为0.49位、5.25秒和15.6%。
Brain Computer Interfaces (BCIs) are systems with great potential for the rehabilitation of people with severe motor injuries. By analyzing a subject’s brain waves, it is possible to detect patterns and translate his “thinking” into device commands, like prosthesis or a robotic arm. This research presents an EEG processing method, which is capable of detecting patterns of the subject’s motor imagery, splitting the patters in left or right hand imagery. The proposed method considers the Round Cosine Transform (RCT), a low computational complexity transform, and an artificial neural network (ANN) module which identifies the patterns. The method has been tested in a real-time (RT) continuous EEG processing experiment simulation, controlling a mouse arrow horizontally on a screen based on the subject’s imagery motor activity. The performance of the proposed method is evaluated in terms of the mutual information (MI), classification time and misclassification rate (%). The achieved results were 0.49 bits, 5.25 s and 15.6%, respectively.