arXiv:2606.16160v1 Announce Type: cross Abstract: Accurately classifying cognitive load from functional near-infrared spectroscopy (fNIRS) signals remains a significant challenge due to temporal variability, inter-subject differences, and sensitivity to preprocessing choices. This study provides a comprehensive evaluation of EEGNet for fNIRS-based cognitive load classification by systematically examining the effects of temporal segmentation strategies (overlapping vs. non-overlapping), window lengths (10s, 20s, 30s), feature extraction methods (Analysis of Variance (ANOVA), Principal Component
The continuous advancements in AI and neurotechnology are pushing the boundaries of cognitive load measurement, addressing previous limitations in accuracy and reliability.
Improved cognitive load classification from fNIRS signals can enhance human-computer interaction, mental health monitoring, and cognitive performance optimization in various fields.
This research refines the methodology for utilizing EEGNet with fNIRS data, making cognitive state assessment more robust and paving the way for practical applications.
- · Neurotechnology researchers
- · Human-computer interaction developers
- · Mental health tech startups
- · AI algorithm developers
- · Traditional subjective cognitive load assessment methods
- · Inaccurate brain-computer interface solutions
More precise and reliable brain-computer interfaces are developed, enhancing control and communication.
Widespread adoption of neurotechnology for real-time cognitive monitoring in high-stakes environments like aviation or surgery becomes feasible.
Personalized cognitive enhancement and intervention strategies emerge, tailored to individual brain states and real-time demands.
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Read at arXiv cs.AI