P300 Detection using Ensemble of SVM for Brain-Computer Interface Application

Abstract

This study provides a novel brain-computer interface (BCI) approach for character recognition. The character recognition task is a two class classification problem. The key objective of the character recognition is to detect the P300 signal from the set of electroencephalogram (EEG) signals. The character is predicted from the detected P300 signal and row/column information of the oddball paradigm. The signal-to-noise ratio (SNR) of the electroencephalogram (EEG) signal is low. Ensemble of classifier is used to reduce the classifier variability and enhance the SNR of the acquired signal. Here ensemble of SVMs (ESVMs) is used a classifier. The distribution of dataset is imbalanced due to its paradigm. A novel approach is applied in this work to balance the dataset. The proposed algorithm is evaluated on dataset IIb of BCI Competition II and dataset II of the BCI Competition III. The experimental results show that the proposed method outperforms the state-of-the-art character recognition performance.