EEG-Based Imagined-Speech Decoding: A Review

Non-invasive neural speech interfaces aim to reconstruct intended words directly from brain activity, providing vital communication channels for individuals with severe dysarthria or locked-in syndrome. Among various modalities, electroencephalography (EEG) remains the most accessible and cost-effective option. Nevertheless, decoding imagined speech from EEG signals remains challenging due to their inherently low signal-to-noise ratio, pronounced inter-subject variability, and the limited size and heterogeneity of available corpora. This review synthesises peer-reviewed studies on EEG-based imagined-speech recognition, encompassing preprocessing pipelines, feature extraction strategies, and deep learning-based decoding methods. Unlike earlier surveys that emphasised isolated algorithmic aspects, this review offers a comprehensive perspective across the entire decoding pipeline from neural acquisition and signal conditioning to representation learning and evaluation. Persistent gaps include inconsistent evaluation protocols, restricted vocabularies, and limited generalisation across subjects. Furthermore, the review summarises key findings and proposes a set of practical recommendations to guide reproducible progress in the field. Finally, it highlights the principal technical challenges of deep-learning-based imagined-speech decoding and outlines promising future research directions.