IEEE

Enhancing Sleep Stage Classification With Single Channel EEG: Feature Extraction and Random Forest-XGBoost Model

Abstract: Sleep stage classification, essential for diagnosing sleep disorders, monitoring neurological health, and advancing cognitive neuroscience. Traditionally involves manual scoring of ...
IEEE

MFENet: A Multi-Feature Extraction Network for Enhanced Emotion Detection Using EEG and STFT

Abstract: Developing computationally efficient models for EEG-based emotion recognition is essential for enabling scalable and responsive brain-computer interface (BCI) systems. However, the inherent ...
eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...