In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts. Common Spatial Pattern (CSP) algorithm has been proved to be effective in BCI for extracting features in motor imagery tasks, but it is prone to overfitting. Many algorithms have been devised to regularize CSP for two class problem, however they have not been effective when applied to multiclass CSP. Outliers present in data affect extracted CSP features and reduces performance of the system. In addition to this non-stationarity present in the features extracted from the CSP present a challenge in classification. We propose a method to identify and remove artifact present in the data during pre-processing stage, this helps in calculating eigenvectors which in turn generates better CSP features. To handle the non-stationarity, Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System (SRIT2NFIS) was proposed in the literature for two class EEG classification problem. This paper extends the SRIT2NFIS to multiclass using Joint Approximate Diagonalization (JAD). The results on standard data set from BCI competition IV shows significant increase in the accuracies from the current state of the art methods for multiclass classification.
翻译:在脑计算机界面(BCI)中,从电脑图(EEG)中产生的数据不是静止的,其信号与噪音比率低,并受到文物的污染。通用空间模式(CSP)算法在BCI中被证明在提取运动图像任务中的特性方面是有效的,但很容易被过度适应。许多算法设计了使CSP对两个类问题的常规化,但在应用到多级CSP时,这些算法并不有效。数据中存在的外端影响提取的CSP特征并降低系统性能。除了从CSP中提取的特征中的非常态性外端点在分类方面是一个挑战。我们提出了一个在预处理阶段确定和删除数据中存在的文物的方法,这有助于计算出能产生更好的CSP特性的静态。为了处理非常态性,文献中提出了自调调的二型神经系统(SRIT2NFIS)在两种EG分类问题上的影响。本文将SRIT2NFIS系统从联合的AQACTA标准化结果从联合AQA级的多级化方法延伸至多级。