Hajipour Sardouie Sepideh, Shamsollahi Mohammad Bagher
Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, Sharif University of Technology Tehran, Iran.
Front Neurosci. 2012 Apr 2;6:42. doi: 10.3389/fnins.2012.00042. eCollection 2012.
The aim of a brain-computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The proposed method was validated in the BCI competition IV, where it obtained the best result among BCI competitors: a classification accuracy of 59.5 and 34.3% for subject 1 and subject 2 on the test data respectively.
脑机接口（BCI）系统的目的是建立一种新的通信系统，该系统将由脑磁图（MEG）等脑信号测量所反映的人类意图转换为输出设备的控制信号。本文提出了一种用于区分在四个方向上手部运动期间记录的MEG信号的算法。这些信号作为BCI竞赛IV的数据集3呈现。所提出的算法有四个主要阶段：预处理、主要特征提取、有效特征选择和分类。分类阶段是线性支持向量机（SVM）和线性判别分析分类器的组合。所提出的方法在BCI竞赛IV中得到了验证，在测试数据上，它在BCI竞争者中获得了最佳结果：受试者1和受试者2的分类准确率分别为59.5%和34.3%。
