Speech Imagery Decoding Using EEG Signals and Deep Learning: A Survey
Liying Zhang, Yueying Zhou, Peiliang Gong, Daoqiang Zhang
Abstract
Speech imagery (SI)-based brain–computer interface (BCI) using electroencephalogram (EEG) signal is a promising area of research for individuals with severe speech production disorders. Recent advances in deep learning (DL) have led to significant improvements in this domain. However, there is a lack of comprehensive review that covers the application of DL methods for decoding imagined speech via EEG. In this article, we survey SI and DL literature to address critical questions regarding preferred paradigms, preprocessing necessity, optimal input formulations, and current trends in DL-based techniques. Specifically, we first search major databases across science and engineering disciplines for relevant studies. Then, we analyze the DL-based techniques applied in SI decoding from five main perspectives: dataset, preprocessing, input formulation, DL architecture, and performance evaluation. Moreover, we summarize the key findings of this work and propose a set of practical recommendations. Finally, we highlight the practical challenges of DL-based imagined speech decoding and suggest future research directions.