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Related Experiment Video

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Assessment and Communication for People with Disorders of Consciousness
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Brain-Computer Interface-Based Communication in the Completely Locked-In State.

Ujwal Chaudhary1, Bin Xia2, Stefano Silvoni3

  • 1Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.

Plos Biology
|February 1, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a new brain-computer interface (BCI) for complete locked-in state (CLIS) patients using functional near-infrared spectroscopy (fNIRS). This novel approach enables communication for individuals with severe paralysis, offering a potential breakthrough for those with amyotrophic lateral sclerosis (ALS).

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Brain-Computer Interface (BCI) Technology
  • Neurological Disorders and Rehabilitation

Background:

  • Complete locked-in state (CLIS) presents a significant communication barrier for individuals with intact cognition but total motor paralysis.
  • Previous neuroelectric brain-computer interface (BCI) attempts have shown limited success in enabling communication for CLIS patients.
  • Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that can lead to CLIS.

Purpose of the Study:

  • To investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) for BCI communication in patients with CLIS.
  • To explore an implicit attentional processing procedure for enabling communication in individuals with severe motor impairments.
  • To establish a novel communication method for ALS patients progressing towards or in CLIS.

Main Methods:

  • Four patients with advanced ALS, including two in CLIS, participated in the study.
  • Functional near-infrared spectroscopy (fNIRS) was employed to measure frontocentral oxygenation changes.
  • A linear support vector machine (SVM) was used for online classification of 'yes'/'no' responses to personal and open-ended questions.

Main Results:

  • Patients learned to communicate by modulating frontocentral oxygenation signals detected by fNIRS, achieving above-chance accuracy (>70%) in classifying responses.
  • Electroencephalography (EEG) and electrooculography (EOG) did not consistently achieve above-chance communication levels.
  • EEG theta-frequency band changes correlated with decreased communication performance, possibly due to attention deficits.

Conclusions:

  • fNIRS-based BCI, utilizing an implicit attentional strategy, offers a promising avenue for communication in CLIS patients.
  • This method represents a potential first step towards overcoming the communication challenges posed by CLIS, particularly in ALS.
  • Further replication and refinement are needed, but the results suggest a significant advancement in assistive communication technology.