# Universal EEG Encoder for Learning Diverse Intelligent Tasks

arXiv: https://arxiv.org/abs/1911.12152 · HTML: https://mlsanity.com/paper/1911.12152/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [MindBigData 2022 A Large Dataset of Brain Signals](https://mlsanity.com/paper/2212.14746/) (2022)
- [Analysis of EEG frequency bands for Envisioned Speech Recognition](https://mlsanity.com/paper/2203.15250/) (2022)
- [Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN](https://mlsanity.com/paper/2402.10115/) (2024)

## Abstract (arXiv)

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.