# Toward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding System

arXiv: https://arxiv.org/abs/2511.07936 · HTML: https://mlsanity.com/paper/2511.07936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Progress in Brain Computer Interfaces: Challenges and Trends](https://mlsanity.com/paper/1901.03442/) (2019)
- [Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on Viseme Decoding](https://mlsanity.com/paper/2501.14790/) (2025)
- [Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer](https://mlsanity.com/paper/2504.03762/) (2025)
- [Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms](https://mlsanity.com/paper/2411.11302/) (2024)

## Abstract (arXiv)

Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless imagined speech electroencephalogram (EEG) decoding system designed for flexibility and everyday use. Our framework focuses on practicality, demonstrating extensibility beyond wired EEG devices to portable, wireless hardware. A user identification module recognizes the operator and provides a personalized, user-specific service. To achieve seamless, real-time operation, we utilize the lab streaming layer to manage the continuous streaming of live EEG signals to the personalized decoder. This end-to-end pipeline enables a functional real-time application capable of classifying user commands from imagined speech EEG signals, achieving an overall 4-class accuracy of 62.00 % on a wired device and 46.67 % on a portable wireless headset. This paper demonstrates a significant step towards truly practical and accessible BCI technology, establishing a clear direction for future research in robust, practical, and personalized neural interfaces.