脑电信号分类的机器学习算法比较研究

Anam Hashmi, B. Khan, Omar Farooq
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摘要

本文比较了线性判别分析、支持向量机(SVM)、多层感知器、随机森林、k近邻、自动编码器等不同的机器学习算法。进行这种比较是为了寻求一种能够产生良好分类精度的鲁棒方法。为此,提出了一种对与想象中的右手运动和放松状态相关的原始脑电图信号进行鲁棒分类的方法,即基于支持向量机的自动编码器。这项研究中使用的脑电图数据集是由德国蒂宾根大学创建的。通过特征工程实现SVM的最佳分类准确率为70.4%。然而,我们提出的自动编码器与支持向量机相结合的方法在不使用任何特征工程技术的情况下产生了相似的65%的精度。这项研究表明,这种运动分类系统可以用于脑机接口系统(BCI),以对机器人设备或外骨骼进行精神控制。
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A Comparative Study of Machine Learning Algorithms for EEG Signal Classification
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM have been compared. This comparison was conducted to seek a robust method that would produce good classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG) signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder with SVM has been proposed. The EEG dataset used in this research was created by the University of Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature engineering. However, our prosed method of autoencoder in combination with SVM produced a similar accuracy of 65% without using any feature engineering technique. This research shows that this system of classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.
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