Non-Invasive BCI for the Decoding of Intended Arm Reaching Movement in Prosthetic Limb Control

Ching-Chang Kuo, Jessica L. Knight, Chelsea A. Dressel, A. Chiu
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引用次数: 13

Abstract

Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide an alternative means of communicat ion with and control over external assistive devices. In general, EEG is insufficient to obtain detailed information about many degrees of freedom (DOF) for arm movements. The main objectives are to design a non-invasive BCI and create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects' visual fixation to the target locations would have litt le or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) classifier to perform single-trial classification of the EEG to decode the intended arm movement in the left , right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310ms after visual stimu lation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to imp rove the classification accuracy fro m 60.11% to 93.91% in the binary class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imag ined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control.
非侵入性脑机接口(BCI)对义肢控制中预期手臂到达运动的解码
基于无创脑电图(EEG)的脑机接口(BCI)能够提供与外部辅助设备通信和控制的替代手段。一般来说,EEG不足以获得手臂运动的多个自由度的详细信息。主要目标是设计一种非侵入性脑机接口,并创建一种信号解码策略,使运动控制有限的人能够对潜在的假肢装置有更多的控制。8名健康受试者被招募来执行视觉线索定向到达任务。眼睛和运动伪影被识别和去除,以确保受试者对目标位置的视觉固定对最终结果的影响很小或没有影响。我们应用Fisher线性判别(FLD)分类器对EEG进行单次分类,以解码在左、右和向前方向上的预期手臂运动(在实际运动开始之前)。在视觉刺激后271 ~ 310ms,脑电信号在PPC区附近的平均幅值是分类效果最好的主要特征。在二元分类(左对右)场景中,发现开发的信号缩放因子将分类准确率从60.11%提高到93.91%。这一结果显示了脑机接口神经义肢应用的巨大前景,因为运动意图解码可以作为图像运动运动分类的前驱,以协助运动残疾康复,如假肢或轮椅控制。
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