Twin Neural Networks for Efficient EEG Signal Classification

摘要

Classification of ElectroEncephaloGram (EEG) signals has found several applications in developing Brain Computer Interfaces (BCIs), as well as other clinical and nonclinical applications based on EEG signals. Processing of EEG signals in this context is challenged by its non-stationarity, high dimensionality and the problem of class imbalance for training classifiers, particularly in case of multi-class classification. Our recent work demonstrated the utility of Twin Support Vector Machine (TWSVM) classifiers for robust classification of imbalanced datasets, specifically EEG signal classification. However, the architecture of the TWSVM is not scalable for large datasets as it involves computing the kernel and matrix inversion operations. In this paper, we present an application of the recently proposed neural network architecture for the Twin SVM, the Twin Neural Network (Twin NN), for robust classification of EEG signals. Results on datasets from BCI competitions illustrate the improved generalization and scalability of the Twin NN for the binary and multi-class classification tasks.