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Multisensory integration task-based age group classification in early-mid adulthood.

Prerna Singh1, Eva Ghanshani2, Pooja Mahajan2

  • 1Bharti School of Telecommunication Technology and Management, Indian Institute of Technology Delhi, Delhi, India. bsz208534@iitd.ac.in.

Medical & Biological Engineering & Computing
|September 22, 2025
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Summary
This summary is machine-generated.

This study reveals how audio-visual integration changes with age, finding early integration between 200-325 ms. Optimized EEG channel selection accurately classifies age groups based on multisensory processing.

Keywords:
AgeAudio-visualERP

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Human Development

Background:

  • Multisensory integration is crucial for perception.
  • Understanding age-related changes in multisensory processing is important for cognitive health.
  • Previous research has not fully elucidated the temporal dynamics of multisensory integration across early to mid-adulthood.

Purpose of the Study:

  • To investigate the temporal dynamics of audio-visual integration in early to mid-adulthood.
  • To assess the impact of temporal asynchrony on multisensory integration.
  • To explore age-related differences in brain topography during multisensory processing.

Main Methods:

  • Analysis of integration times (0-500 ms) in five regions of interest (ROIs).
  • Behavioral analysis of temporal asynchrony effects on audio-visual integration.
  • Electroencephalography (EEG) and event-related potential (ERP) analysis with random forest classification for age group differentiation.

Main Results:

  • Early audio-visual integration consistently occurs between 200 and 325 ms across age groups.
  • Audio stimuli integrate slower than visual stimuli; audio-visual integration falls in between.
  • Age-related differences in multisensory processing were observed, particularly in middle-aged adults, with distinct integration times for audio-leading versus visual-leading stimuli.
  • ERP-based channel selection achieved 98.3% accuracy in age group classification using only 13 channels during the audio-leading task.

Conclusions:

  • Multisensory integration exhibits specific temporal dynamics that vary with age and stimulus modality.
  • Optimized EEG channel selection significantly improves the efficiency and accuracy of age group classification based on multisensory processing.
  • This study highlights the feasibility of using minimal electrodes and simple features for effective multisensory integration analysis in adults.