PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification

• Published in: Cognitive robotics Vol. 5; pp. 211 - 225
• Main Authors: Wang, Teng, Li, Fenglian, Yang, Jia, Jia, Wenhui, Hu, Fengyun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2025; KeAi Communications Co. Ltd
• Subjects: Convolutional neural network; Discrete wavelet transform; EEG; Empirical mode decomposition; Stroke; Stroke; Empirical mode decomposition; Discrete wavelet transform; EEG; Convolutional neural network

Stroke classification is crucial for timely diagnosis and treatment, as it helps differentiate between hemorrhagic and ischemic strokes, which require distinct clinical interventions. This paper proposes a stroke classification method using multi-channel electroencephalography (EEG) data. Unlike sin...