# Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

arXiv: https://arxiv.org/abs/1709.08820 · HTML: https://mlsanity.com/paper/1709.08820/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 11 of 11)

- [Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text](https://mlsanity.com/paper/2405.00726/) (2024)
- [SEE: Semantically Aligned EEG-to-Text Translation](https://mlsanity.com/paper/2409.16312/) (2024)
- [Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.09006/) (2024)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders](https://mlsanity.com/paper/2602.18478/) (2026)
- [Genetic algorithm for feature selection of EEG heterogeneous data](https://mlsanity.com/paper/2103.07117/) (2021)
- [A Penny for Your Thoughts: Decoding Speech from Inexpensive Brain Signals](https://mlsanity.com/paper/2511.04691/) (2025)
- [Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding](https://mlsanity.com/paper/2603.03312/) (2026)
- [The evolution of AI approaches for motor imagery EEG-based BCIs](https://mlsanity.com/paper/2210.06290/) (2022)
- [RatioWaveNet: A Learnable RDWT Front-End for Robust and Interpretable EEG Motor-Imagery Classification](https://mlsanity.com/paper/2510.21841/) (2025)
- [NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals](https://mlsanity.com/paper/2604.09654/) (2026)

## Abstract (arXiv)

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subjects active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems, is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large- scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the- art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.