Higashi Hiroshi, Tanaka Toshihisa
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4271-4. doi: 10.1109/EMBC.2012.6346910.
We propose a method of sparsifying EEG signals in the time domain for common spatial patterns (CSP) which are often used for feature extraction in brain computer interfaces (BCI). For accurate classification, it is important to analyze the period of time when a BCI user performs a mental task. We address this problem by optimizing the CSP cost with a time sparsification that removes unnecessary samples from the classification. We design a cost function that has CSP spatial weights and time window as optimization parameters. To find these parameters, we use alternating optimization. In an experiment on classification of motor-imagery EEG signals, the proposed method increased classification accuracy by 6% averaged over five subjects.
我们提出了一种在时域中对脑电图（EEG）信号进行稀疏化处理的方法，用于脑机接口（BCI）中常用的公共空间模式（CSP）进行特征提取。为了实现准确分类，分析BCI用户执行心理任务的时间段非常重要。我们通过一种时间稀疏化方法来优化CSP代价，从而解决这个问题，这种方法可以从分类中去除不必要的样本。我们设计了一个代价函数，该函数将CSP空间权重和时间窗口作为优化参数。为了找到这些参数，我们使用交替优化方法。在一项运动想象EEG信号分类实验中，所提出的方法使五个受试者的平均分类准确率提高了6%。
