James-Mc1ntyre/DeepLearningProject
Using Deep Learning techniques to classify Motor Imagery Electroencephalography (EEG) signals
This project helps researchers and practitioners classify imagined movements from brainwave data. It takes raw or minimally processed electroencephalography (EEG) signals from motor imagery tasks as input and outputs a classification of the imagined movement (e.g., left hand, right hand, feet, or tongue). Neurologists, brain-computer interface developers, and cognitive scientists can use this to better understand brain activity during motor imagination.
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Use this if you need to accurately classify specific motor imagery tasks from EEG data using advanced deep learning techniques.
Not ideal if you are looking for simple, classical machine learning methods or if your primary interest is in understanding feature extraction through autoencoders or transfer learning, as these approaches were less successful here.
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Oct 26, 2022
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