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Cross-subject Cognitive State Evaluation in Aviation Multi-Task Scenarios
- Citation Author(s):
-
XinyuZhang
- Submitted by:
- Xinyu Zhang
- Last updated:
- Tue, 04/15/2025 - 23:58
- DOI:
- 10.21227/nf5n-zx64
- Data Format:
- Research Article Link:
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- License:
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Abstract
This repository contains resources for EEG data processing and cognitive load recognition using a Multi-Head Attention EEGNet model. It includes original EEG data, MATLAB code for preprocessing, and Python code for classification.
With the ethics approval obtained from our institution, this study acquired 30 subjects aged between 18 to 29 to conduct research. Informed written consents were attained from all participants. The selection of participants follows a standardized and rigorous protocol that they have to meet the following requirements:
1. All participants are right-handed.
2. All participants are with normal hearing.
3. All participants have normal or correct-to-normal vision.
4. All participants have adequate sleep before experiments.
5. All participants are asked to avoid strenuous exercise before experiments.
6. All participants are in good health, with no history of mental or intelligent illness.
This project aims to process EEG data and perform cognitive load recognition using advanced neural network techniques. The repository is organized into three main components:
- EEG Data: Raw EEG data collected for cognitive load studies.
- MATLAB Code: Scripts for preprocessing EEG data to prepare it for analysis.
- Python Code: Implementation of a Multi-Head Attention EEGNet model for cognitive load classification.
For more details, please visit: https://github.com/d-lab438/Multi-channels-eegnet
