A Comparison of CNNs and LSTMs for EEG Signal Classification

Non-invasive EEG devices have shown novel applications from neuro-biological exploration to robotic control. Controlling robotic movements using brain activity requires accurate processing of real time multi-channel data for classification into multiple classes for actuating the robot. Multiple networks ranging from convolutional and recurrent neural networks have been used to classify the time-encoded analog data stream. In this work, we study the classification of a 14-channel EEG device using convolutional neural networks (CNN) and long-short term memory (LSTM) for wrist motor response classification. Varying network structures suggested that CNNs consistently outperformed LSTMs in accuracy by approximately 10%. In the second step, we evaluated the relative importance of the channels where a subset of the EEG channels were provided as inputs to the classifier and the results showed that the CNN performance dropped quicker with a reduced number of channels. We also identified a set of channels with the least effect on classification performance while comparing the individual contributions of the channels in the classification output. The results of this work may help in choosing network architectures and sensitive brain regions for future low power EEG applications.