Electroencephalography (EEG) is a complex bioelectrical signal. Analysis of which can provide researchers with useful physiological information. In order to recognize and classify EEG signals, a pattern recognition method for optimizing the support vector machine (SVM) by using improved squirrel search algorithm (ISSA) is proposed. The EEG signal is preprocessed, with its time domain features being extracted and directed to the SVM as feature vectors for classification and identification. In this paper, the method of good point set is used to initialize the population position, chaos and reverse learning mechanism are introduced into the algorithm. The performance test of the improved squirrel algorithm (ISSA) is carried out by using the benchmark function. As can be seen from the statistical analysis of the results, the exploration ability and convergence speed of the algorithm are improved. This is then used to optimize SVM parameters. ISSA-SVM model is established and built for classification of EEG signals, compared with other common SVM parameter optimization models. For data sets, the average classification accuracy of this method is 85.9%. This result is an improvement of 2–5% over the comparison method.

中文翻译：

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基于SVM和改进松鼠搜索算法的脑电信号分类

脑电图 (EEG) 是一种复杂的生物电信号。分析其中可以为研究人员提供有用的生理信息。为了对脑电信号进行识别和分类，提出了一种利用改进的松鼠搜索算法（ISSA）优化支持向量机（SVM）的模式识别方法。对 EEG 信号进行预处理，提取其时域特征并将其作为特征向量引导至 SVM 进行分类和识别。本文采用良好点集的方法初始化种群位置，在算法中引入混沌和逆向学习机制。改进的松鼠算法（ISSA）的性能测试是使用基准函数进行的。从结果的统计分析可以看出，提高了算法的探索能力和收敛速度。然后将其用于优化 SVM 参数。与其他常见的SVM参数优化模型相比，ISSA-SVM模型被建立和建立用于EEG信号的分类。对于数据集，该方法的平均分类准确率为85.9%。该结果比比较方法提高了 2-5%。