Classification of EEG during imagined mental tasks by forecasting with Elman Recurrent Neural Networks

The ability to classify EEG recorded while a subject performs varying imagined mental tasks may lay the foundation for building usable Brain-Computer Interfaces as well as improve the performance of EEG analysis software used in clinical settings. Although a number of research groups have produced EEG classifiers, these methods have not yet reached a level of performance that is acceptable for use in many practical applications. We assert that current approaches are limited by their ability to capture the temporal and spatial patterns contained within EEG. In order to address these problems, we propose a new generative technique for EEG classification that uses Elman Recurrent Neural Networks. EEG recorded while a subject performs one of several imagined mental tasks is first modeled by training a network to forecast the signal a single step ahead in time. We show that these models are able to forecast EEG well with an RMSE as low as 0.110. A separate model is then trained over EEG belonging to each class. Classification of previously unseen data is performed by applying each model and assigning the class label associated with the network that produced the lowest forecasting error. This approach is tested on EEG collected from two able-bodied subjects and one subject with a high-level spinal cord injury. Classification rates as high as 93.3% are achieved for a two-task problem with decisions made every second yielding a bitrate of 38.7 bits per minute.