A low-channel EEG-to-speech conversion approach for assisting people with communication disorders

Abstract

Brain–Computer Interface (BCI) technology has emerged as a promising solution for individuals with communication disorders. However, current electroencephalography (EEG) to speech systems typically require high-channel EEG equipment (64＋ channels), limiting their accessibility in resource-constrained environments. This paper implements a novel low-channel EEG-to-speech framework that effectively operates with only 6 EEG channels. By leveraging a generator-discriminator architecture for speech reconstruction, our system achieves a Character Error Rate (CER) of 64.24%, outperforming baseline systems that utilize 64 channels (68.26% CER). We further integrate Undercomplete Independent Component Analysis (UICA) for channel reduction, maintaining comparable accuracy (64.99% CER) while reducing computational complexity from 6 channels to 4 channels. This breakthrough demonstrates the feasibility of efficient speech reconstruction from minimal EEG inputs, potentially enabling more widespread deployment of BCI technology in resource-limited healthcare settings.