3 research outputs found
Universal EEG Encoder for Learning Diverse Intelligent Tasks
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