Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use. A speech brain-computer interface enables a clinical-trial participant with amyotrophic lateral sclerosis to control a communication board and home devices without model retraining or baseline calibration. Six intuitive speech commands are accurately detected and decoded (median accuracy: 90.59%) from a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex throughout a 3-month study period.image

Automated summary

Brain-computer interfaces (BCIs) can accurately and reliably control assistive devices for patients with neurological disorders. This study demonstrates that a chronically implanted ECoG-based speech BCI can accurately detect and decode speech commands without the need for retraining or recalibration, supporting its feasibility for long-term use.