Toward robust, reproducible, and widely accessible intracranial speech brain-computer interfaces: a comprehensive narrative review of neural mechanisms, hardware, algorithms, evaluation, clinical pathways and future directions

This is the authors' abstract. We don't add key points for this paper.

Intracranial speech brain-computer interfaces (BCIs) offer a promising route for restoring communication in individuals with severe, chronic, and otherwise irreversible motor and speech impairments who cannot communicate adequately using conventional assistive technologies, but clinical translation remains limited by fragmented and heterogeneous evidence, as well as unresolved design trade-offs across neuroscience, hardware, algorithms, validation methods, and clinical integration. This narrative review synthesizes recent progress across four key domains in intracranial speech neuroprosthetics: i) the neural mechanisms underlying overt, attempted (including silent miming and attempts without observable movement), and imagined speech (speech imagery without an attempt to articulate); ii) decision-oriented hardware comparisons of surgically implanted recording modalities, including microelectrode array (MEA), electrocorticography (ECoG), and stereotactic electroencephalography (SEEG); iii) experimental strategies for cross-subject generalization; and iv) advances in neural decoding, including sequence models, attention-based architectures (e.g., transformers), articulatory intermediate representations, and language-prior-assisted frameworks. We highlight persistent bottlenecks, including limited transfer without individual calibration, long-term non-stationarity and recalibration burden, heterogeneous and non-comparable evaluation practices, limited naturalistic expressivity, and the variable signal-to-noise ratio (SNR) and limited temporal alignment of neural activity in imagined speech decoding. Our contributions are threefold: (1) an end-to-end, decision-oriented synthesis that links neural representations to recording choices, experimental design, decoding model architectures, and translational constraints; (2) a structured framework organized around five coupled design questions, accompanied by a unified evaluation framework and a conceptual cross-linguistic, cross-task benchmark template that integrates objective, perceptual, expressive, conversational, and longitudinal metrics; and (3) user-centered translational guidance that includes agency-preserving shared control, verifiable performance priorities, and scenario-specific minimum viable product (MVP) profiles for differentiating between reliability-first home communication and fidelity-first conversational speech restoration. We conclude with a call for larger multilingual, multi-center longitudinal datasets; harmonized benchmarks; adaptive yet interpretable decoders; prospective clinical validation; and transparent data-sharing and reporting practices with robust ethical safeguards. These efforts are essential to accelerate the safe and equitable deployment of speech neuroprostheses.