Mental Task Classification Using Electroencephalogram Signal
This work addresses EEG-based brain-computer interface applications for tasks like assistive technology, but it is incremental as it combines existing deep learning methods.
The paper tackles mental task classification from EEG signals by proposing a mixed LSTM model with a CNN decoder, achieving a testing accuracy of 70%, which outperforms standard CNN, LSTM, and GRU models with accuracies ranging from 51% to 62%.
This paper studies the classification problem on electroencephalogram (EEG) data of mental tasks, using standard architecture of three-layer CNN, stacked LSTM, stacked GRU. We further propose a novel classifier - a mixed LSTM model with a CNN decoder. A hyperparameter optimization on CNN shows validation accuracy of 72% and testing accuracy of 62%. The stacked LSTM and GRU models with FFT preprocessing and downsampling on data achieve 55% and 51% testing accuracy respectively. As for the mixed LSTM model with CNN decoder, validation accuracy of 75% and testing accuracy of 70% are obtained. We believe the mixed model is more robust and accurate than both CNN and LSTM individually, by using the CNN layer as a decoder for following LSTM layers. The code is completed in the framework of Pytorch and Keras. Results and code can be found at https://github.com/theyou21/BigProject.