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Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI

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Veröffentlicht in:Bioengineering vol. 12, no. 1 (2025), p. 25
1. Verfasser: Xianglong Wan
Weitere Verfasser: Sun, Yue, Yao, Yiduo, Wan Zuha Wan Hasan, Dong, Wen
Veröffentlicht:
MDPI AG
Schlagworte:
Feature extraction
Accuracy
Signal analysis
Channels
Assessments
Electrodes
Artificial neural networks
Signal processing
Cognition
Electroencephalography
Geriatrics
Cognitive ability
Performance evaluation
Cognitive tasks
Cognition & reasoning
Robust control
EEG
Classification
Signal quality
Signal classification
Quality of life
Data collection
Information processing
Females
Permutations
Neural networks
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Abstract:With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states. This study proposes a novel approach for EEG signal classification, utilizing Permutation Conditional Mutual Information (PCMI) for feature extraction and a Multi-Scale Squeezed Excitation Convolutional Neural Network (MSSECNN) model for classification. Specifically, the MSSECNN classifies spatial cognitive states into two classes—before and after cognitive training—based on EEG features. First, the PCMI extracts nonlinear spatial features, generating spatial feature matrices across different channels. SENet then adaptively weights these features, highlighting key channels. Finally, the MSCNN model captures local and global features using convolution kernels of varying sizes, enhancing classification accuracy and robustness. This study systematically validates the model using cognitive training data from a brain-controlled car and manually operated UAV tasks, with cognitive state assessments performed through spatial cognition games combined with EEG signals. The experimental findings demonstrate that the proposed model significantly outperforms traditional methods, offering superior classification accuracy, robustness, and feature extraction capabilities. The MSSECNN model’s advantages in spatial cognitive state classification provide valuable technical support for early identification and intervention in cognitive decline.
ISSN:2306-5354
DOI:10.3390/bioengineering12010025
Quelle:Engineering Database