Neural Noise

Motor intention quantification in disorders of consciousness by multimodal BCI (EEG and fNIRS)

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  • Multimodal BCI combining EEG and fNIRS quantifies motor intention in patients with disorders of consciousness 1.
  • The approach supports hybrid modality use and clinical BCI in ICU-like settings; peer-reviewed in Wiley 1. 1

Weekly enrichment (2026-07-20)

  • The primary study (CNS Neuroscience & Therapeutics, cns.70679) synchronously acquired EEG and fNIRS during a left-/right-hand motor-imagery BCI paradigm to quantify residual motor intention in disorders of consciousness (DoC).2
  • It recruited 8 healthy controls and 11 DoC patients (6 minimally conscious state, 5 vegetative state) from Beijing Tiantan Hospital and Hangzhou Mingzhou Brain Rehabilitation Hospital, stratified by the Coma Recovery Scale–Revised (CRS-R).2
  • The combined EEG–fNIRS framework achieved three-class classification accuracies of about 97.47% for healthy controls, 93.28% for the MCS group, and 76.19% for the VS group, significantly outperforming single-modality decoding (p < 0.05 across patient groups).2
  • Acquisition used 32 Ag/AgCl EEG channels (10–10 system, 1 kHz, impedance <5 kΩ) plus a NirSmartII-3000A fNIRS system with 22 sources and 15 detectors (45 optical channels, 3.0 cm spacing, 730/850 nm, ~11 Hz sampling).2
  • Event-related desynchronization (ERD) from EEG emerged as the more sensitive biomarker of motor-intention intensity, distinguishing MCS from VS better than raw BCI classification accuracy.2
  • The bedside, metal-implant-tolerant approach targets the roughly 40% behavioral misdiagnosis rate of CRS-R by providing an objective neural marker of covert command-following.2
  • A companion Chinese-journal study using the same EEG–fNIRS motor-BCI approach in 14 DoC patients (7 MCS, 7 VS) reported EEG accuracies of 93.28% (MCS) and 76.19% (VS), fNIRS accuracies of 53.72% and 49.11%, and combined accuracies of 95.56% and 87.38%, with significant ERD differences during left-hand tasks (p < 0.001).3
  • Both reports converge on hybrid EEG–fNIRS BCI as a promising ICU/bedside tool for detecting cognitive-motor dissociation and improving consciousness diagnosis.3

Footnotes

  1. https://news.google.com/rss/articles/CBMiakFVX3lxTE5GMVVSV1VURkN5QXVGOWRXSWprUTN4TTF1YXpza1o5T3lneEkwcjhhSVJuSU9uRFNVai03WGdycXkxYU13TEI2T2VKNE1VeTdTeWVFdkZtT3RacTQ4cXdDa2VSc1RMV1V4RWc?oc=5 ↩ ↩2 ↩3

  2. https://doi.org/10.1002/cns.70679 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  3. https://pmc.ncbi.nlm.nih.gov/articles/PMC12236222/ ↩ ↩2

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