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- W4360999278 abstract "Electroencephalography (EEG) signals, which record brain activity, are known to be very useful in diagnosing brain-related conditions. However, manual examination of these EEG signals has various limitations, including reliance on physician experience and subjectivity of assessment. Therefore, an automatic EEG recognition system would be useful to assist neurologists in the early screening and diagnosis of schizophrenia. In this study, we proposed a novel EEG data mapping method that eliminated the process of manual feature design and pioneered the use of Vision Transformer as feature extractor and classifier for early identification of schizophrenia. First, the raw EEG data were pre-processed, including artifact removal and normalization. Then, we mapped 1D EEG sequences into 3D picture form based on EEG acquisition electrode distribution maps and different brain lobe partitions while preserving the temporal and spatial nature of EEG data. Afterward, we used a lightweight Vision Transformer model LeViT for recognition, which has an architecture combining convolutional layers and attention mechanisms that facilitate learning spatial–temporal features in the mapped 3D pictures. Finally, we conducted extensive experiments on two evaluation criteria: subject-dependent and subject-independent. Considering the generalization of the model, we used ten-fold cross-validation and leave-one-subject-out cross-validation to evaluate the performance of the model, respectively. The average accuracy for subject-independent is 98.99% and the average accuracy for subject-dependent is 85.04%. The results show that the proposed framework may serve as a diagnostic tool to assist clinicians in the detection of EEG pathology for early treatment." @default.
- W4360999278 created "2023-03-30" @default.
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- W4360999278 date "2023-08-01" @default.
- W4360999278 modified "2023-10-16" @default.
- W4360999278 title "Automatic detection of schizophrenia based on spatial–temporal feature mapping and LeViT with EEG signals" @default.
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- W4360999278 doi "https://doi.org/10.1016/j.eswa.2023.119969" @default.
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