A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI

This paper determined the best combination that maximizes the classification accuracy of single-channel electroencephalogram (EEG)-based motor imagery brain–computer interfaces (BCIs). BCIs allow people including completely locked-in patients to communicate with others without actual movements of body. Whereas EEGs are usually observed by multiple electrodes, single-channel measurement has been recently studied for gaining the simplicity of use. However, existing single-channel BCI studies have evaluated the performance on their own, private datasets that are not accessible from other researchers. Therefore, it remains a practical challenge to determine the optimal combination of channel, feature, and classifier using a public dataset. For the assessment, we used an open-access database (BCI competition IV dataset 2a) and a 10-fold cross-validation procedure. We found that support vector machine or multilayer perceptron with power spectrum or single-channel common spectral patterns of C3 or C4 position showed high classification accuracies in all subjects (mean: 63.5±0.4%, maximum: 86.6±0.4%).

本文确定了使基于单通道脑电图（EEG）的运动想象脑机接口（BCI）分类准确率最大化的最佳组合。脑机接口使包括完全闭锁综合征患者在内的人们能够在身体没有实际运动的情况下与他人交流。虽然脑电图通常由多个电极进行观测，但最近人们也在研究单通道测量以获得使用的简便性。然而，现有的单通道脑机接口研究都是在其自身的私有数据集上评估性能，其他研究人员无法获取这些数据集。因此，使用公共数据集确定通道、特征和分类器的最佳组合仍然是一个实际的挑战。为了进行评估，我们使用了一个开放获取的数据库（脑机接口竞赛IV数据集2a）和10折交叉验证程序。我们发现，使用C3或C4位置的功率谱或单通道公共频谱模式的支持向量机或多层感知器在所有受试者中都显示出较高的分类准确率（平均值：63.5±0.4%，最大值：86.6±0.4%）。