eeyhsong/EEG-Transformer
i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.
This project helps neuroscientists and BCI researchers classify different categories of Electroencephalograph (EEG) data. It takes raw or preprocessed EEG signals as input, applies spatial filtering and attention-based transformations across both channels and time, and outputs a classification of the EEG activity. This is designed for researchers working with brain-computer interfaces or analyzing brainwave patterns.
331 stars. No commits in the last 6 months.
Use this if you need to accurately classify EEG signals, especially in scenarios where global relationships across brain regions and time are important.
Not ideal if you are working with non-physiological 2D signals, or if you prefer traditional CNN-based methods for EEG classification.
Stars
331
Forks
40
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 23, 2023
Commits (30d)
0
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