EEG Signal Classificaiton Method Based on Fuzzy Confidence Causal Power and Multi-stream Ensemble Neural Networks
FAN Min1,2,3, CHEN Qiuyu1,2,3, LI Jinhai1,2,3
1. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500; 2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500; 3. Yunnan Key Laboratory of Complex Systems and Brain-Inspired Intelligence, Kunming University of Science and Technology, Kunming 650500
Abstract：The automatic identification of epileptic electroencephalogram（EEG） signals is of paramount importance for clinical diagnosis. However, high-dimensional redundancy, non-stationarity, and difficulties in cross-scenario generalization are commonly encountered in automatic EEG detection. To address these issues, a EEG signal classificaiton method based on fuzzy confident causal power and multi-stream ensemble neural networks is proposed. First, a feature selection algorithm based on Rényi entropy and fuzzy confident causal power（FCCP） is proposed to mitigate high-dimensional redundancy. Fuzzy Rényi entropy and the theory of confident causal power are introduced to quantify the bidirectional causal effects between features and labels. Key features with strong causal interpretability are selected by utilizing data structure constraints. Second, the formal context and the space of causal power attribute network are constructed to mine underlying topological features. Thus, network-based modeling of unstructured data is achieved. Finally, a multi-stream ensemble neural network（MENN） classifier is developed. The deep fully-connected stream, the wide stream and the narrow-deep stream are integrated to simultaneously capture local details and global dependencies. Multi-scale feature representations are fused through a concatenation layer. The experiments on Bonn and CHB-MIT clinical datasets demonstrate that the proposed method achieves stable and superior performance under multi-task settings, exhibiting significant robustness in distinguishing seizure signals. The effectiveness of the combination of confident causal power-based feature selection with multi-stream integrated learning is verified.
范敏, 陈秋羽, 李金海. 基于模糊置信度因果力和多流集成神经网络的脑电信号分类方法[J]. 模式识别与人工智能, 2026, 39(4): 330-347.
FAN Min, CHEN Qiuyu, LI Jinhai. EEG Signal Classificaiton Method Based on Fuzzy Confidence Causal Power and Multi-stream Ensemble Neural Networks. Pattern Recognition and Artificial Intelligence, 2026, 39(4): 330-347.
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