MAEEG: Masked Auto-encoder for EEG Representation Learning
This paper was accepted at the Workshop on Learning from Time Series for Health at NeurIPS 2022. Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transformer architecture. We found that MAEEG can learn representations that significantly improve sleep stage classification (∼ 5% accuracy…
Detecting delirium isn’t easy, but it can have a big payoff: speeding essential care to patients, leading to quicker and surer recovery. Improved detection also reduces the need for long-term skilled care, enhancing the quality of life for patients while decreasing a major financial burden. In the U.S., caring for…
The research team at The University of Texas at Austin created a noninvasive electroencephalogram (EEG) sensor that they installed in a Meta VR headset that can be worn comfortably for long periods. The EEG measures the brain's electrical activity during the immersive VR interactions.