IEEE

Enhanced Seizure Prediction Using XGBoost and EEG Data: A Machine Learning Approach with increasing Model Metrics

Abstract: Epilepsy is a neurological disorder marked by abnormal brain activity. Normally, brain cells (neurons) communicate with each other through electrical signals. In epilepsy, these signals ...
IEEE

Rolling Bearing Fault Diagnosis Based on VMD-CNN-SLSTM Model

Abstract: Convolutional neural networks (CNN) are limited by the local receptive field, making it difficult to capture the shock features of the cross period. In view of this limitation, a ...
IEEE

Characterization of Cortical Connectivity in the Deception State With a Data-Driven Network Model Based on EEG Signal

Abstract: This study investigates the pattern of information interaction at the cortical level during deception, aiming to reveal the cognitive processes involved in the deception task. Our study ...
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

LSTA-CNN: A Lightweight Spatiotemporal Attention-Based Convolutional Neural Network for ASD Diagnosis Using EEG

Abstract: Electroencephalography (EEG) is an effective assessment tool to identify autism spectrum disorders with low cost, and deep learning has been applied in EEG analysis for extracting meaningful ...