Brain-Computer Interface (BCI) is an emerging technology in medical diagnosis and rehabilitation. In this study, by the acquisition of Electroencephalogram (EEG) signals from 30 healthy participants who perform four different hand movements, necessary features are extracted and classified to determine their accuracies. Statistical time domain features are extracted from the mu and beta frequency band. The Event related desynchronization (ERD)/Event related synchronization (ERS) measurements are extracted, from which it was evident that both mu and beta frequency bands are more efficient in the C3 channel. By applying the Paired Samples t-test, the extracted features are analyzed and were determined to have a 95% significant level of difference between the mu and beta band, being statistically efficient in the beta band of the C3 channel. By employing different classifiers such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Naïve Bayesian classifier and Binary Decision Tree (BDT) algorithms on both channel’s mu and beta frequency bands, it was observed that the performance of beta frequency band classifiers shows 90% accuracy in binary class classification. In the comparative study of all these classifiers, LDA and Naïve Bayes show above 95% accuracy for binary class classification.

中文翻译：

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脑波分类用于发散的手部运动

脑机接口（BCI）是医学诊断和康复中的新兴技术。在这项研究中，通过从30位健康的参与者中采集的脑电图（EEG）信号进行四种不同的手部动作，提取必要的特征并将其分类以确定其准确性。从mu和beta频段提取统计时域特征。提取了事件相关的不同步（ERD）/事件相关的同步（ERS）测量值，从中可以明显看出，μ和β频段在C3通道中均更为有效。通过应用配对样本t-test，分析提取的特征并确定其在mu和beta频段之间有95％的显着差异水平，在C3通道的beta频段上具有统计上的效率。通过在信道的mu和beta频带上采用不同的分类器，例如支持向量机（SVM），线性判别分析（LDA），二次判别分析（QDA），朴素贝叶斯分类器和二叉决策树（BDT）算法，可以观察到Beta频段分类器的性能在二进制分类中显示90％的准确性。在所有这些分类器的比较研究中，LDA和朴素贝叶斯显示二元类分类的准确性超过95％。