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

TL;DR

This paper presents a real-time wireless EEG decoding system for imagined speech, advancing BCI towards practical, portable, and user-specific applications with promising accuracy levels.

Contribution

It introduces a portable, wireless EEG decoding framework with user identification, enabling real-time imagined speech classification for practical BCI use.

Findings

Achieved 62% accuracy on wired EEG devices.

Achieved 47% accuracy on portable wireless EEG.

Demonstrated real-time, user-specific imagined speech decoding.

Abstract

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.