Brain Computer Interfaces (BCI) have become very popular with Electroencephalography (EEG) being one of the most commonly used signal acquisition techniques. A major challenge in BCI studies is the individualistic analysis required for each task. Thus, task-specific feature extraction and classification are performed, which fails to generalize to other tasks with similar time-series EEG input data. To this end, we design a GRU-based universal deep encoding architecture to extract meaningful features from publicly available datasets for five diverse EEG-based classification tasks. Our network can generate task and format-independent data representation and outperform the state of the art EEGNet architecture on most experiments. We also compare our results with CNN-based, and Autoencoder networks, in turn performing local, spatial, temporal and unsupervised analysis on the data.
@article{arxiv.1911.12152,
title = {Universal EEG Encoder for Learning Diverse Intelligent Tasks},
author = {Baani Leen Kaur Jolly and Palash Aggrawal and Surabhi S Nath and Viresh Gupta and Manraj Singh Grover and Rajiv Ratn Shah},
journal= {arXiv preprint arXiv:1911.12152},
year = {2019}
}