Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions

A team of researchers led by Dongyi He has published a landmark review paper on arXiv, providing a comprehensive roadmap for developing practical intracranial language Brain-Computer Interfaces (BCIs). These systems aim to decode speech directly from brain signals to restore communication for individuals with conditions like locked-in syndrome or severe paralysis. The review synthesizes fragmented progress across neuroscience, hardware engineering, and machine learning, creating an end-to-end, decision-oriented framework that links neural representations to clinical deployment.

The paper meticulously compares recording hardware—microelectrode arrays (MEAs), electrocorticography (ECoG), and stereotactic EEG (SEEG)—and evaluates advanced decoding algorithms, including sequence models and transformers. It identifies major bottlenecks hindering widespread use: weak generalization across different subjects, the recalibration burden due to non-stationary neural signals, and a critically low signal-to-noise ratio (SNR) when decoding covert or imagined speech, especially in tonal languages.

To address these challenges, the authors propose a unified evaluation framework and a benchmark template. This template integrates objective, perceptual, and conversational metrics to standardize the field. Crucially, they provide user-centered translational guidance, outlining distinct "minimum viable product" profiles for different clinical needs, such as reliability-first systems for basic home communication versus fidelity-first systems for conversational speech restoration.