XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms
Stereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. This capability is deeply intertwined with cognitive brain functions. To explore the impact of stereograms with varied mo...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10843300/ |
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| author | Tingting Zhang Xu Yan Xin Chen Yi Mao |
| author_facet | Tingting Zhang Xu Yan Xin Chen Yi Mao |
| author_sort | Tingting Zhang |
| collection | DOAJ |
| description | Stereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. This capability is deeply intertwined with cognitive brain functions. To explore the impact of stereograms with varied motions on brain activities, we collected Electroencephalography (EEG) signals evoked by Dynamic Random Dot Stereograms (DRDS). To effectively classify the EEG signals induced by DRDS, we introduced a novel hybrid neural network model, XCF-LSTMSATNet, which integrates an XGBoost Channel Feature Optimization Module with the EEGNet and an LSTM Self-Attention Modules. Initially, in the channel selection phase, XGBoost is employed for preliminary classification and feature weight analysis, which can enhance our channel selection strategy. Following this, EEGNet employs deep convolutional layers to extract spatial features, while separable convolutions are subsequently used to derive high-dimensional spatial-temporal features. Meanwhile, the LSTMSAT Module, with its capability to learn long-term dependencies in time-series signals, is deployed to capture temporal continuity information. The incorporation of the self-attention mechanism further amplifies the model’s ability to grasp long-distance dependencies and enables dynamic weight allocation to the extracted features. In the end, both temporal and spatial features are integrated into the classification module, enabling precise prediction across three categories of EEG signals. The proposed XCF-LSTMSATNet was extensively tested on both a custom dataset and the public datasets SRDA and SRDB. The results demonstrate that the model exhibits solid classification performance across all three datasets, effectively showcasing its robustness and generalization capabilities. |
| format | Article |
| id | doaj-art-84ae74a75a59491f885d1a870ac756e1 |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-84ae74a75a59491f885d1a870ac756e12025-01-28T00:00:18ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013350251310.1109/TNSRE.2025.352999110843300XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot StereogramsTingting Zhang0https://orcid.org/0000-0002-4944-2323Xu Yan1https://orcid.org/0009-0006-4970-4772Xin Chen2Yi Mao3https://orcid.org/0000-0002-8624-772XKey Laboratory of Maritime Intelligent Cyberspace Technology, Ministry of Education, Hohai University, Nanjing, ChinaKey Laboratory of Maritime Intelligent Cyberspace Technology, Ministry of Education, Hohai University, Nanjing, ChinaDepartment of Cardiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, ChinaKey Laboratory of Maritime Intelligent Cyberspace Technology, Ministry of Education, Hohai University, Nanjing, ChinaStereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. This capability is deeply intertwined with cognitive brain functions. To explore the impact of stereograms with varied motions on brain activities, we collected Electroencephalography (EEG) signals evoked by Dynamic Random Dot Stereograms (DRDS). To effectively classify the EEG signals induced by DRDS, we introduced a novel hybrid neural network model, XCF-LSTMSATNet, which integrates an XGBoost Channel Feature Optimization Module with the EEGNet and an LSTM Self-Attention Modules. Initially, in the channel selection phase, XGBoost is employed for preliminary classification and feature weight analysis, which can enhance our channel selection strategy. Following this, EEGNet employs deep convolutional layers to extract spatial features, while separable convolutions are subsequently used to derive high-dimensional spatial-temporal features. Meanwhile, the LSTMSAT Module, with its capability to learn long-term dependencies in time-series signals, is deployed to capture temporal continuity information. The incorporation of the self-attention mechanism further amplifies the model’s ability to grasp long-distance dependencies and enables dynamic weight allocation to the extracted features. In the end, both temporal and spatial features are integrated into the classification module, enabling precise prediction across three categories of EEG signals. The proposed XCF-LSTMSATNet was extensively tested on both a custom dataset and the public datasets SRDA and SRDB. The results demonstrate that the model exhibits solid classification performance across all three datasets, effectively showcasing its robustness and generalization capabilities.https://ieeexplore.ieee.org/document/10843300/EEGDRDSLSTMEEGNetXGBoost |
| spellingShingle | Tingting Zhang Xu Yan Xin Chen Yi Mao XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms IEEE Transactions on Neural Systems and Rehabilitation Engineering EEG DRDS LSTM EEGNet XGBoost |
| title | XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms |
| title_full | XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms |
| title_fullStr | XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms |
| title_full_unstemmed | XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms |
| title_short | XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms |
| title_sort | xcf lstmsatnet a classification approach for eeg signals evoked by dynamic random dot stereograms |
| topic | EEG DRDS LSTM EEGNet XGBoost |
| url | https://ieeexplore.ieee.org/document/10843300/ |
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