Scalable human neuron networks reveal brain-like rhythms and how drugs reshape them

An EEG (electroencephalogram) is a painless test that uses small sensors placed on the scalp to measure the brain's ...
2 Minute Medicine

Deep-learning models displayed expert-level accuracy in detecting epileptiform discharges on electroencephalogram (EEG) recordings

Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of human ...

Cleveland Clinic teaching artificial intelligence to read brain waves and detect seizures in seconds

AI technology analyzes 24 hours of brain wave data in seconds, a task that takes trained specialists two hours to complete.
IEEE

Deep Learning Model for Analyzing EEG Signal Analysis

Abstract: To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, ...
Frontiers

Resting-state EEG and machine learning to investigate cortical connectivity as a biomarker in chronic mTBI

Introduction: Mild traumatic brain injury (mTBI) is a heterogeneous condition with long-term sequelae, yet diagnosis in the chronic stage remains limited by reliance on acute criteria and subjective ...
GitHub

xw1216/EEG-FM-Bench

🌟 What is EEG-FM-Bench? EEG-FM-Bench addresses a critical gap in neuroscience AI research: the lack of standardized evaluation frameworks for EEG foundation models. As these models rapidly ...
IEEE

Multi-Seizure Classification Using Transformer- Based Attention Models and Pseudo-Labeling Techniques in EEG Analysis

Abstract: Traditional seizure classification models from electroencephalogram (EEG) data often struggle to achieve high accuracy in multi-seizure classification due to their complex, and non-linear ...