What type of study is this?

This is a Human Clinical Trial study (also classified as: Pre-Registered Trial).

December 1, 2025Open Access

Motor Intention Quantization for Patients With Disorders of Consciousness by Multimodal BCI Combining Electroencephalography and Functional Near‐Infrared Spectroscopy

Key Points

The multimodal brain-computer interface approach enhances the diagnostic accuracy of disorders of consciousness.
Machine-learning results demonstrated significant improvement with a mean AUC of 0.69 ± 0.10 compared to unimodal techniques.
Assessment utilized combined electroencephalography and functional near-infrared spectroscopy to analyze consciousness levels.
This approach highlights the potential for integrating neurophysiological and hemodynamic biomarkers into clinical diagnostics.

Abstract

ABSTRACT Objective The 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. Methods Eleven 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. Results The 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 EEGERD 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: fNIRSACC, EEGACC, fNIRSₛlope, fNIRScentroid, EEGERD, fNIRSᵢntegral, and fNIRSₘean. Conclusion The 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 Registration Clinical Trial Registry: ChiCTR2400085830

Read Full Paperexternally

Mark Helpful

Bookmark

Relay

View Full Paper