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

Abstract

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.

Don’t put words in my mouth: Speech perception can generate False Positive activation of a speech BCI

“…Recent developments in speech decoding are very promising for individuals who can no longer speak (Angrick et al, 2023; Luo et al, 2023; Metzger et al, 2022, 2023; Moses et al, 2021; Wairagkar et al, 2023; Willett et al, 2023). However, those results are typically achieved in controlled lab settings, and not in the daily lives of intended end users.…”

“…It has been demonstrated that BCIs can be used by individuals with ALS to control a communication application on a computer by means of a binary click based on attempted hand movements (Oxley et al, 2021; Vansteensel et al, 2016), with long-term stability (Pels et al, 2019). Another way to control a device is through independent multidimensional cursor navigation based on the production of speech commands (Luo et al, 2023). Others have focused on direct speech decoding for communication.…”

Object surface roughness/texture recognition using machine vision enables for human-machine haptic interaction

“…Currently, there is still a wide gap between the functionality and utility of the robots and the users' expectations. An acceptable level of intention control function can be obtained by translating the muscle electrical activities of peripheral limbs (Farina et al, 2023) or the central nerve activities of the brain (Luo et al, 2023) into the commands of an intelligent robotic device via multi-channel neural control interfaces (Makin et al, 2023). However, the existing interactive robots generally have common shortcomings, such as poor performance in interactive control, insufficient ability in humanmachine collaboration, and a lack of active sense capability (Yang et al, 2019).…”

Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study

“…Reliable long-term decoding results from ECoG signals are a strong indicator of the stability of the underlying recording system [17,18], but only a few studies have specifically focused on individuals with ALS [19][20][21]. ECoG recordings from the upper limb area of the sensorimotor cortex (SMC) in an individual with late-stage ALS were stable for 36 months and allowed for several years of BCI home use [19].…”

“…The work of Silversmith et al in plug-and-play cursor control, and Benabid et al in long-term exoskeleton control demonstrate the ability of upper limb ECoG BCIs to function over many days without the need for retraining [18,22,23]. Luo et al achieved stable speech BCI decoding for three months without recalibration using an implanted ECoG BCI [21]. Mean bandpower remained relatively stable across both high and low frequencies in a study by Moly et al, where bimanual motor-based avatar and exoskeleton tasks were reliably carried out for six and four months, respectively, without recalibration [17].…”