Neuropsychological and neurophysiological aspects of brain-computer-interface (BCI) control in paralysis

J Physiol. 2021 May;599(9):2351-2359. doi: 10.1113/JP278775. Epub 2020 Mar 28.

Abstract

Brain-computer interfaces (BCIs) aim to help paralysed patients to interact with their environment by controlling external devices using brain activity, thereby bypassing the dysfunctional motor system. Some neuronal disorders, such as amyotrophic lateral sclerosis (ALS), severely impair the communication capacity of patients. Several invasive and non-invasive brain-computer interfaces (BCIs), most notably using electroencephalography (EEG), have been developed to provide a means of communication to paralysed patients. However, except for a few reports, all available BCI literature for the paralysed (mostly ALS patients) describes patients with intact eye movement control, i.e. patients in a locked-in state (LIS) but not a completely locked-in state (CLIS). In this article we will discuss: (1) the fundamental neuropsychological learning factors and neurophysiological factors determining BCI performance in clinical applications; (2) the difference between LIS and CLIS; (3) recent development in BCIs for communication with patients in the completely locked-in state; (4) the effect of BCI-based communication on emotional well-being and quality of life; and (5) the outlook and the methodology needed to provide a means of communication for patients who have none. Thus, we present an overview of available studies and recent results and try to anticipate future developments which may open new doors for BCI communication with the completely paralysed.

Keywords: amyotrophic lateral sclerosis (ALS); brain-computer interface (BCI); communication; completely locked-in state (CLIS); electroencephalogram; functional near-infrared spectroscopy (NIRS); invasive BCI; locked-in state (LIS).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amyotrophic Lateral Sclerosis*
  • Brain
  • Brain-Computer Interfaces*
  • Computers
  • Electroencephalography
  • Humans
  • Paralysis
  • Quality of Life