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首页> 外文期刊>Brain Sciences >A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface
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A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

机译：基于遗传的特征选择方法在脑机界面左右手运动图像识别中的应用

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摘要

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.
机译：脑电图检查是对数百万个神经元产生的大脑电活动的一种非侵入式测量。脑电图分析中的特征提取是可能导致准确的脑部心理状态分类的核心问题。本文提出了一种新的特征选择方法，该方法基于遗传算法和人工神经网络作为分类器，可改进运动图像脑机接口的左右手运动识别。首先使用适当的滤波对原始脑电信号进行预处理。之后，基于频谱和时间信号分量进行特征提取，从而构造特征向量。由于各种特征可能不准确并误导了分类器，从而降低了整体系统性能，因此所提出的方法从较大的特征空间中识别出特征子集，从而降低了分类器的错误率。实验结果表明，该方法能够将特征量减少到0.5％（即被忽略的特征量可以达到99.5％），同时提高了分类器的准确性，灵敏性，特异性和精确度。

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