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Conference Paper

Universal EEG Encoder for Learning Diverse Intelligent Tasks

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Jolly, B., Aggrawal, P., Nath, S., Gupta, V., Grover, M., & Shah, R. (2019). Universal EEG Encoder for Learning Diverse Intelligent Tasks. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) (pp. 213-218). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/21.11116/0000-0009-64C5-8
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.