Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI
Gorde:
| Argitaratua izan da: | Bioengineering vol. 12, no. 1 (2025), p. 25 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , |
| Argitaratua: |
MDPI AG
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiketak: |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3159429414 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12010025 |2 doi | |
| 035 | |a 3159429414 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Xianglong Wan |u School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Education, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China | |
| 245 | 1 | |a Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Signal analysis | ||
| 653 | |a Channels | ||
| 653 | |a Assessments | ||
| 653 | |a Electrodes | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Cognition | ||
| 653 | |a Electroencephalography | ||
| 653 | |a Geriatrics | ||
| 653 | |a Cognitive ability | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Cognitive tasks | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Robust control | ||
| 653 | |a EEG | ||
| 653 | |a Classification | ||
| 653 | |a Signal quality | ||
| 653 | |a Signal classification | ||
| 653 | |a Quality of life | ||
| 653 | |a Data collection | ||
| 653 | |a Information processing | ||
| 653 | |a Females | ||
| 653 | |a Permutations | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Sun, Yue |u School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China | |
| 700 | 1 | |a Yao, Yiduo |u School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China; Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia | |
| 700 | 1 | |a Wan Zuha Wan Hasan |u Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia | |
| 700 | 1 | |a Dong, Wen |u School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Education, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 1 (2025), p. 25 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159429414/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159429414/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159429414/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |