Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review
Abstract
The loss of communication constitutes a critical challenge for people living with paralysis. Brain-computer interfaces (BCIs) paired with artificial intelligence (AI) provide an opportunity to restore this ability. This systematic review examined the use of AI to decode speech from brain signals through both invasive and non-invasive neural interfaces. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we reviewed 115 studies published between 2019 and 2025 to extract data on acquisition protocols, signal preprocessing, and AI architectures. The quality of each study was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The results indicated that the invasive approach achieved a higher median multiclass classification accuracy than the non-invasive method (77.7%, interquartile range (IQR): 61.4-93.2% vs. 73.0%, IQR: 49.3-89.2%; N = 82 studies with comparable multiclass metrics), computed from the best-performing model per study across heterogeneous task types, vocabulary sizes, and predominantly subject-dependent evaluation paradigms (84.3% of studies). However, this narrow gap in raw accuracy (4.7% points) should not be interpreted as a direct cross-modality performance ranking, as it obscures substantial differences in task complexity (invasive studies typically decoded larger vocabularies and continuous speech), evaluation paradigm, and participant population. Additionally, the hybrid…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 82 participants or records.
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