EEG-Based Imagined-Speech Decoding: A Review

Non-invasive neural speech interfaces aim to reconstruct intended words from brain activity, offering critical communication options for individuals with severe dysarthria or locked-in syndrome. Among the available recording modalities, electroencephalography (EEG) remains the most accessible and cost-effective choice for long-term brain–computer interface (BCI) applications. Decoding imagined speech from EEG, however, remains difficult because of low signal-to-noise ratio, pronounced inter-subject variability, and the small, heterogeneous corpora that are currently available. This review adopts a narrative methodology to synthesise peer-reviewed studies on EEG-based imagined-speech decoding. Relevant articles were identified through keyword-based searches in major digital libraries and were included if they used non-invasive EEG, explicitly instructed imagined or covert speech, and reported quantitative decoding performance. The selected studies are organised along the processing pipeline, from experimental paradigms and data acquisition to preprocessing, feature extraction, representation learning, and classification. Across this body of work, binary imagined-speech tasks that rely on carefully designed time–frequency features and shallow classifiers often report accuracies above 80 percent, whereas multi-class word or phoneme recognition exhibits a much wider spread of performance and remains highly sensitive to dataset design and evaluation protocol. Recent trends favour convolutional and recurrent neural networks, temporal convolutional networks, and transfer learning strategies, which improve performance on some datasets but do not yet resolve fundamental issues of restricted vocabularies, inconsistent evaluation practices, and limited cross-subject generalisation. The review distils these observations into practical recommendations for dataset construction, model design, and evaluation protocols and outlines research directions aimed at more robust and clinically meaningful EEG-based imagined-speech BCIs.