Neural networks : the official journal of the International Neural Network Society
Apr 28, 2025
Electroencephalogram (EEG)-based brain network analysis has shown promise in brain disease research by revealing the complex connectivity among brain regions. However, existing methods struggle to fully utilize the large amounts of unlabeled data to ...
International journal of neural systems
Apr 28, 2025
Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years, significant progress has been made in this field. However, the predictive performance of existing ...
International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Apr 28, 2025
OBJECTIVE: Temporale lobe and occipito-temporal cortical areas play an important role in facial emotion perception (FEP). FEP might be represented by event-related brain oscillations. In patients with temporal lobe epilepsy (TLE), impairment of FEP w...
Neural networks : the official journal of the International Neural Network Society
Apr 25, 2025
Multimodal emotion recognition focuses on the prediction of emotions using text, visual and acoustic modalities, and some results have been generated in this field. Previous approaches fall short in two aspects, one is the processing of complementary...
Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting rese...
. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in fea...
The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning due to memory loss, cognitive decline, and behavioral problems. By using synchrosqueezing representat...
Self-supervised learning (SSL) is a potent method for leveraging unlabelled data. Nonetheless, EEG signals, characterised by their low signal-to-noise ratio and high-frequency attributes, often do not surpass fully-supervised techniques in cross-subj...
This study focuses on epilepsy detection using hybrid CNN-SVM and DNN-SVM models, combined with feature dimensionality reduction through PCA. The goal is to evaluate the effectiveness and performance of these models in accurately identifying epilepti...
Diverse evidence supports the theory that bilingual language users have language-invariant representations of concepts and grammatical forms such as argument structure. Here we extend that work to test the representation of morphosyntactic features a...
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