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Dual modal data-driven virtual reality-based mild cognitive Impairment assessment using MCIformer.

Yanjie Zhang1, Yang Pan2, Shanshan Feng3

  • 1Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, 999077, Hong Kong Special Administrative Region.

Computer Methods and Programs in Biomedicine
|March 13, 2026
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Summary
This summary is machine-generated.

This study introduces a novel Virtual Reality (VR) assessment for early detection of Mild Cognitive Impairment (MCI) by combining movement data and brain activity. The dual-modal approach significantly improves diagnostic accuracy for cognitive decline.

Keywords:
KinectMCIMachine LearningVRfNIRS

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Area of Science:

  • Neuroscience
  • Medical Technology
  • Artificial Intelligence

Background:

  • Mild Cognitive Impairment (MCI) assessment is crucial for early intervention against Alzheimer's disease (AD).
  • Virtual Reality (VR) offers engaging and ecologically valid cognitive assessments.
  • Existing VR methods often miss subtle motor and neural indicators of cognitive decline.

Purpose of the Study:

  • To develop a dual-modal, data-driven VR assessment integrating kinematic and functional near-infrared spectroscopy (fNIRS) data for MCI detection.
  • To capture subtle motor deficits and neural connectivity changes indicative of early cognitive impairment.
  • To enhance the accuracy and comprehensiveness of MCI assessment tools.

Main Methods:

  • Developed a VR system collecting synchronized kinematic and fNIRS data from healthy and MCI participants.
  • Extracted kinematic features (smoothness, coordination, stability) from movement trajectories.
  • Analyzed fNIRS data to represent functional brain networks and interregional connectivity.
  • Proposed MCIformer, a dual-modal fusion model using Transformers for kinematic sequences and Graph Transformers for fNIRS networks.

Main Results:

  • The dual-modal system achieved 90% accuracy in MCI classification.
  • This significantly outperformed models using only kinematic data (80%) or fNIRS data (85%).
  • Integration of motor patterns and brain connectivity enhances classification by providing complementary information.

Conclusions:

  • The VR-based dual-modal approach shows potential for accurate, scalable early MCI diagnosis in community settings.
  • This method supports the development of advanced brain-behavior monitoring systems for cognitive health.
  • The findings highlight the value of integrating diverse data modalities for robust cognitive assessment.