Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

The task of classifying EEG signals for self-paced Brain Computer Interface (BCI) applications is extremely challenging. This difficulty in classification of self-paced data stems from the fact that the system has no clue about the start time of a control task and the data contains a large number of periods during which the user has no intention to control the BCI. Therefore, to improve the performance of the BCI, it is imperative to exploit the characteristics of the EEG data as much as possible. For motor imagery based self-paced BCIs, during motor imagery task the EEG signal of each subject goes through several internal state changes. Applying appropriate classifiers that can exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an algorithm which is able to capture the temporal correlation of the EEG signal. We compare the performance of our algorithm that is based on neural network conditional random fields to two well-known dynamic classifiers, the Hidden Markov Models and Conditional Random Fields and to the static classifier, Support Vector Machines. We compare these methods using the data from SM2 dataset, and we show that our algorithm yields results that are considerably superior to the other approaches in terms of the Area Under the Curve (AUC) of the BCI system.