Department of Mechatronics Engineering, National University of Sciences & Technology, H-12, Islamabad, Pakistan.
Biomed Res Int. 2018 May 20;2018:2695106. doi: 10.1155/2018/2695106. eCollection 2018.
Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.
脑机接口（BCI）通过各种电生理信号来确定用户的意图。这些信号，即慢皮质电位，是从头皮上记录的，而皮质神经元活动则是通过植入的电极记录的。本文专注于设计一个嵌入式系统，该系统用于使用脑电图（EEG）信号控制上肢假肢的手指运动。这是我们之前研究的后续，该研究探讨了分类手指的三种运动（拇指运动、食指运动和第一运动）的最佳方法。二阶逻辑回归分类器表现出最高的分类准确性，而功率谱密度（PSD）则用作滤波信号的特征。EEG 信号数据集是使用 14 通道电极耳机（一种非侵入性 BCI 系统）从右利手、神经完整的志愿者身上记录的。通过使用“Arduino Uno”微控制器进行滤波，保留了包含大部分运动数据的 Mu（通常称为 alpha 波）和 Beta 节律（8-30 Hz），然后使用二阶逻辑回归获得平均分类准确性为 70%。
