GitHub

mahdiyoussef/stress-peaks-detection

This project implements an advanced deep learning approach for predicting stress peaks in EEG (Electroencephalogram) signals using a hybrid CNN-LSTM neural network architecture. The system combines ...
IEEE

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications

Abstract: The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical ...
Healio

VIDEO: Wearable EEG system provides real-time data on patients with epilepsy

Please provide your email address to receive an email when new articles are posted on . A wearable, portable electroencelphalogram, or EEG, system will allow clinicians to rapidly review real-time ...
IEEE

Speed: Scalable Preprocessing of EEG Data for Self-Supervised Learning

Abstract: Electroencephalography (EEG) research typically focuses on tasks with narrowly defined objectives, but recent studies are expanding into the use of unlabeled data within larger models, ...
GitHub

EEG gradient artefact correction pre-processing function

Code implementation of the moving average filter described in Ferreira, José L et al. (2016). Possibly translating the FASTR function from the fMRIB matlab package that already performs this ...
The American Journal of Managed Care

Automated Processing EEG Algorithm Predicts 1-Year Seizure Recurrence in Patients

Predictions for identifying 1-year seizure recurrence performed significantly better in electroencephalography (EEG) without interictal epileptiform discharges. An automated processing algorithm ...