Deep Learning for Real time EEG Artifact Detection in Wearables

Abstract

Electroencephalography (EEG) has become a valuable tool for monitoring brain activity in both clinical and consumer applications. However, EEG signals collected from wearable devices are often disrupted by artifacts such as eye blinks, muscle movements, and external noise, which can severely compromise the accuracy of real-time analysis. Traditional methods for artifact detection and removal rely on manual techniques or simple filtering, making them unsuitable for continuous, real-time applications, particularly in mobile and wearable devices. This study explores the use of deep learning for real-time EEG artifact detection in wearables. Leveraging advanced techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, the research investigates how these models can effectively identify and eliminate artifacts while preserving the integrity of brainwave data. Unlike conventional methods, deep learning models can be trained to automatically detect noise patterns, improving the speed and accuracy of real-time EEG analysis.