Speech Imagery Decoding Using EEG Signals and Deep Learning: A Survey
IEEE Transactions on Cognitive and Developmental Systems(2024)
Abstract
Speech imagery-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 paper, we survey speech imagery 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 speech imagery 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.
MoreTranslated text
Key words
Speech imagery,Electroencephalogram (EEG),deep learning (DL),brain-computer interface (BCI)
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined