Deep learning methods for EEG-based speech classification and decoding: A PRISMA review

Electroencephalography (EEG)-based speech brain–computer interfaces (BCIs) have gained increasing research interest as a potential means to restore or decode speech for individuals with severe communication impairments, particularly with the recent integration of deep learning techniques. This systematic review, conducted in accordance with the PRISMA 2020 guidelines, provides a comprehensive and quantitative overview of deep learning methods applied to EEG and intracranial EEG (iEEG) speech processing tasks published between 2018 and 2025. A systematic search was conducted across Scopus, IEEE Xplore, ScienceDirect, Web of Science, and PubMed, yielding 1,148 records. Following duplicate removal, screening, and eligibility assessment, 80 peer-reviewed original research articles were included. Studies were organized by task type (speech classification, spectrogram reconstruction, and speech synthesis), neural signal type (non-invasive EEG versus invasive electrocorticography (ECoG) and stereo-electroencephalography (sEEG)), and model architecture to enable structured comparison. Results demonstrate that deep learning models significantly outperform traditional methods in speech classification tasks, while spectrogram reconstruction and speech synthesis remain challenging, particularly for non-invasive EEG. Invasive recordings consistently yield superior performance for reconstruction and synthesis tasks. Despite methodological advances, only a limited number of studies address real-time feasibility or cross-subject generalization, highlighting persistent barriers to clinical translation. Overall, this review provides an integrated, task-oriented synthesis of deep learning–based neural speech decoding and highlights the key methodological and translational barriers that must be addressed to enable robust, real-time, and clinically viable speech neuroprosthetic systems.