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https://github.com/jinglescode/ssvep-multi-task-learning
Using multi-task learning to capture signals simultaneously from the fovea efficiently and the neighboring targets in the peripheral vision generate a visual response map. A calibration-free user-independent solution, desirable for clinical diagnostics. A stepping stone for an objective assessment of glaucoma patients’ visual field.
https://github.com/jinglescode/ssvep-multi-task-learning
bci brain-computer-interface cnn convolutional-neural-network deep-learning eeg multitask-learning pytorch ssvep
Last synced: 3 months ago
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Using multi-task learning to capture signals simultaneously from the fovea efficiently and the neighboring targets in the peripheral vision generate a visual response map. A calibration-free user-independent solution, desirable for clinical diagnostics. A stepping stone for an objective assessment of glaucoma patients’ visual field.
- Host: GitHub
- URL: https://github.com/jinglescode/ssvep-multi-task-learning
- Owner: jinglescode
- License: bsd-2-clause
- Created: 2020-04-23T07:37:10.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-09-20T08:22:45.000Z (over 2 years ago)
- Last Synced: 2024-10-05T00:34:48.935Z (4 months ago)
- Topics: bci, brain-computer-interface, cnn, convolutional-neural-network, deep-learning, eeg, multitask-learning, pytorch, ssvep
- Language: Python
- Homepage: https://jinglescode.github.io/ssvep-multi-task-learning/
- Size: 6.75 MB
- Stars: 35
- Watchers: 2
- Forks: 4
- Open Issues: 1