Dataset Information

Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.

ABSTRACT: Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-in syndrome due to brain stem stroke and an individual with tetraplegia secondary to amyotrophic lateral sclerosis (ALS) used a simple communication BCI based on intracortical local field potentials (LFPs) for 76 and 138 days, respectively, without recalibration and without significant loss of performance. BCI spelling rates of 3.07 and 6.88 correct characters/minute allowed the participants to type messages and write emails. Our results indicate that people with locked-in syndrome could soon use a slow but reliable LFP-based BCI for everyday communication without ongoing intervention from a technician or caregiver. NEW & NOTEWORTHY This study demonstrates, for the first time, stable repeated use of an intracortical brain-computer interface by people with tetraplegia over up to four and a half months. The approach uses local field potentials (LFPs), signals that may be more stable than neuronal action potentials, to decode participants' commands. Throughout the several months of evaluation, the decoder remained unchanged; thus no technical interventions were required to maintain consistent brain-computer interface operation.

SUBMITTER: Milekovic T

PROVIDER: S-EPMC6093965 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.

Milekovic Tomislav T Sarma Anish A AA Bacher Daniel D Simeral John D JD Saab Jad J Pandarinath Chethan C Sorice Brittany L BL Blabe Christine C Oakley Erin M EM Tringale Kathryn R KR Eskandar Emad E Cash Sydney S SS Henderson Jaimie M JM Shenoy Krishna V KV Donoghue John P JP Hochberg Leigh R LR

Journal of neurophysiology 20180425 1

Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-i ...[more]

PMID: 29694279

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Project description:BackgroundBrain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.MethodsWe present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains.ResultsFirst, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot's neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability.ConclusionWe firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.

Dataset Information

Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.

Publications

Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.

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