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

Tomislav MilekovicAnish A SarmaDaniel BacherJohn D SimeralJad SaabChethan PandarinathBrittany L SoriceChristine BlabeErin M OakleyKathryn R TringaleEmad EskandarSydney S CashJaimie M HendersonKrishna V ShenoyJohn P DonoghueLeigh R Hochberg
Published in: Journal of neurophysiology (2018)
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.
Keyphrases
  • amyotrophic lateral sclerosis
  • cerebral ischemia
  • resting state
  • white matter
  • case report
  • deep learning
  • functional connectivity
  • atrial fibrillation
  • brain injury
  • machine learning