Using AI to Remove Noise from EEG Data
What is it about?
Electroencephalographic (EEG) signals provide insights into brain activity, but they often suffer from unwanted disturbances called artifacts. These artifacts can arise from muscle movements or eye blinks, complicating the interpretation of neural data. Traditional methods for removing artifacts face challenges: manual removal isn’t always feasible, and standard denoising techniques struggle when artifact frequencies overlap with genuine neural responses. To address this, we developed a deep learning (DL)-based EEG denoising model. This neural model learns noise patterns and adapts convolutional filters based on prior knowledge of noise spectral features. In other words, it “learns” how to distinguish actual EEG signals from artifact-related data using frequency information.
Why is it important?
The architecture developed, with a unique design compared to traditional models, leverages prior knowledge about noise spectral features to dynamically compute optimal convolutional filters. These filters effectively remove multiple types of artifacts, including muscle and ocular disturbances. The model performs comparably or even better than existing approaches, considering both temporal and spectral metrics. And here’s the kicker: it achieves this without requiring specific training for each artifact type. In essence, this research provides a robust and adaptable solution for enhancing EEG data quality, benefiting both clinical and recreational applications