An Experimental Analysis of Various Deep Learning Architectures for the Classification of Cognitive Stimuli based EEG Signals.

The human brain functions through electrical signals. By measuring these signals, one can monitor brain activity and gain insights into the brain function of the subject. An electroencephalogram (EEG) allows one to monitor brain activity by having the subject wear an array of sensors on their head. This process is frequently used to diagnose medical conditions such as epilepsy. In recent years, there have been efforts to use EEG signals in concert with deep learning to create a brain computer interface (BCI). Such a device would enable the wearer to communicate to a system via brain signals. While such a system would not be so advanced as to enable the translation of complex thoughts, it would enable a user to command a machine to perform a small number of functions. The objective of this paper was to develop and optimize recurrent neural network architectures for use with a brain computer interface. Using EEG data collected from subjects, a variety of neural network models were created to learn from the data. The models that were used were simple recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU). This paper proposes a novel approach to EEG signal classification, demonstrating the capabilities of recurrent networks which are seldom explored for this purpose. This study produced promising results for recurrent models, obtaining a 91% accuracy with the 4-layer LSTM architecture. This presents a solid foundation for the argument that LSTM and similar architectures are feasible for BCI applications. [ABSTRACT FROM AUTHOR]