论文标题
在电动机bci分类中应用的双重拉格朗日方法的性能和常见的空间模式
Performance of Dual-Augmented Lagrangian Method and Common Spatial Patterns applied in classification of Motor-Imagery BCI
论文作者
论文摘要
基于汽车的脑部计算机界面（MI-BCI）有潜力成为神经康复的开创性技术，重建非肌肉通信以及针对患有神经疾病和残疾患者的患者的命令，以及在临床实践之外，用于视频游戏控制和其他娱乐目的。但是，由于使用的脑电图信号的嘈杂性质，可靠的BCI系统需要专门的程序才能优化和提取。 This paper compares the two approaches, the Common Spatial Patterns with Linear Discriminant Analysis classifier (CSP-LDA), widely used in BCI for extracting features in Motor Imagery (MI) tasks, and the Dual-Augmented Lagrangian (DAL) framework with three different regularization methods: group sparsity with row groups (DAL-GLR), dual-spectrum (DAL-DS) and l1-norm regularization （DAL-L1）。该测试已针对7名健康受试者进行，每次进行5个BCI-MI会议。初步结果表明，DAL-GLR方法的表现优于标准CSP-LDA，降低了6.9％的错误分类误差（p-value = 0.008），并证明了MI-BCI的DAL框架的优势。
Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation, the reestablishment of non-muscular communication and commands for patients suffering from neuronal disorders and disabilities, but also outside of clinical practice, for video game control and other entertainment purposes. However, due to the noisy nature of the used EEG signal, reliable BCI systems require specialized procedures for features optimization and extraction. This paper compares the two approaches, the Common Spatial Patterns with Linear Discriminant Analysis classifier (CSP-LDA), widely used in BCI for extracting features in Motor Imagery (MI) tasks, and the Dual-Augmented Lagrangian (DAL) framework with three different regularization methods: group sparsity with row groups (DAL-GLR), dual-spectrum (DAL-DS) and l1-norm regularization (DAL-L1). The test has been performed on 7 healthy subjects performing 5 BCI-MI sessions each. The preliminary results show that DAL-GLR method outperforms standard CSP-LDA, presenting 6.9% lower misclassification error (p-value = 0.008) and demonstrate the advantage of DAL framework for MI-BCI.
