ArticleCNS neuroscience & therapeutics2025
Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near-Infrared Spectroscopy.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational predictive processing models of consciousness: a systematic review of non-invasive brain signal analysis in disorders of consciousness.Frontiers in computational neuroscience · 2026Pooled it
- Multifunctional material platforms for neural interfaces: active orchestration of dynamic foreign body response across implantation lifetimes.Bioactive materials · 2026Review
- [Application and perspective of novel auditory intervention paradigms based on verbal and nonverbal stimuli for severe traumatic brain injury].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort.Journal of translational medicine · 2026Observational
- Decoding multi-class motor attempt from the affected unilateral limbs in chronic stroke patients.Journal of neuroengineering and rehabilitation · 2026Article
- Prospective validation protocol for artificial intelligence-assisted standardization of Coma Recovery Scale-Revised scores using electroencephalography and clinical data in disorders of consciousness.Frontiers in medicine · 2026Article
- Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near-Infrared Spectroscopy.CNS neuroscience & therapeutics · 2025Article
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15 authors.
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Abstract
objectiveThe current application of single-modality electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS) to assess consciousness levels in patients with disorders of consciousness (DoC) has garnered significant attention. However, the diagnostic accuracy of unimodal approaches remains suboptimal. Therefore, this study aims to apply the multimodal fusion technology of EEG and fNIRS to the clinical diagnosis of DoC patients.
methodsEleven patients with DoC (six with a minimally conscious state [MCS] and five with a vegetative state [VS]) were enrolled. The motor intention-based brain-computer interface (MI-BCI) paradigm was adopted. EEG and fNIRS were recorded simultaneously. The synchronous states of EEG and fNIRS were analyzed, including time-frequency analysis, event-related desynchronization (ERD), and changes in oxy-hemoglobin (HbO)/de-oxygenated (HbR)/total hemoglobin (HbT) content. A multimodal method combining EEG and fNIRS was used to classify DoC patients.
resultsThe machine-learning results of the MI-BCI model showed that the EEG-fNIRS multimodal approach was superior to single-modality techniques in the diagnosis of healthy controls (HC), MCS, and VS. The multimodal model achieved a mean AUC of 0.69 ± 0.10, significantly outperforming both unimodal EEG (0.43 ± 0.19; p < 0.01) and standalone fNIRS (0.63 ± 0.10; p < 0.05). The EEG_ERD index of left-handed MI-BCI significantly differentiated the MCS and VS groups. Meanwhile, for the classification tasks of HC, MCS, and VS, the importance ranking of the indicators was as follows: fNIRS_ACC, EEG_ACC, fNIRS_slope, fNIRS_centroid, EEG_ERD, fNIRS_integral, and fNIRS_mean.
conclusionThe integration of multimodal MI-BCI paradigms demonstrates clinical potential in evaluating consciousness levels, while the synergistic combination of neurophysiological and hemodynamic biomarkers provides a robust framework for enhancing the precision of bedside diagnostic protocols.
trial registrationClinical Trial Registry: ChiCTR2400085830.
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