  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.
