Translating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding

TL;DR

This paper introduces a novel EEG-based BCI paradigm using a codebook of mental tasks and a dynamics-enhanced decoding network, achieving high accuracy in translating mental imagery into characters without external stimulation.

Contribution

It proposes a new EEG paradigm with a codebook of mental tasks and eye states, combined with a TSLD network for improved decoding accuracy, surpassing existing methods.

Findings

Achieves five times higher accuracy than direct imagination methods.

TSLD network improves decoding performance by approximately 8.5%.

Performance remains stable and improves with continued use.

Abstract

Advancements in non-invasive electroencephalogram (EEG)-based Brain-Computer Interface (BCI) technology have enabled communication through brain activity, offering significant potential for individuals with motor impairments. Existing methods for decoding characters or words from EEG recordings either rely on continuous external stimulation for high decoding accuracy or depend on direct intention imagination, which suffers from reduced discrimination ability. To overcome these limitations, we introduce a novel EEG paradigm based on mental tasks that achieves high discrimination accuracy without external stimulation. Specifically, we propose a codebook in which each letter or number is associated with a unique code that integrates three mental tasks, interleaved with eye-open and eye-closed states. This approach allows individuals to internally reference characters without external stimuli while maintaining reasonable accuracy. For enhanced decoding performance, we apply a Temporal-Spatial-Latent-Dynamics (TSLD) network to capture latent dynamics of spatiotemporal EEG signals. Experimental results demonstrate the effectiveness of our proposed EEG paradigm which achieves five times higher accuracy over direct imagination. Additionally, the TSLD network improves baseline methods by approximately 8.5%. Further more, we observe consistent performance improvement throughout the data collection process, suggesting that the proposed paradigm has potential for further optimization with continued use.