EEG Classification of Covert Speech Using Regularized Neural Networks

Communication using brain-computer interfaces (BCIs) can be non-intuitive, often requiring the performance of a conversation-irrelevant task such as hand motor imagery. In this paper, the reliability of electroencephalography (EEG) signals in discriminating between different covert speech tasks is investigated. Twelve participants, across two sessions each, were asked to perform multiple iterations of three differing mental tasks for 10 s each: unconstrained rest or the mental repetition of the words yes or no. A multilayer perceptron (MLP) artificial neural network (ANN) was used to classify all three pairwise combinations of yes, no, and rest trials and also for ternary classification. An average accuracy of 75.7% +/- 9.6 was reached in the classification of covert speech trials versus rest, with all participants exceeding chance level (57.8%). The classification of yes versus no yielded an average accuracy of 63.2 +/- 6.4 with ten participants surpassing chance level (57.8%). Finally, the ternary classification yielded an average accuracy of 54.1% +/- 9.7 with all participants exceeding chance level (39.1%). The proposed MLP network provided significantly higher accuracies compared to some of the most common classification techniques in BCI. To our knowledge, this is the first report of using ANN for the classification of EEG covert speech across multiple sessions. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.