orvindemsy/ea-tca-mi-bci
Using combination EA (Euclidean Alignment) and TCA (Transfer Component Analysis) for transfer learning approach for MI-based BCI. This work served as a research project for master's degree completion.
This project helps BCI researchers and developers reduce the time and effort needed to set up a new motor imagery-based Brain-Computer Interface (BCI) system for individual users. It takes existing brain signal data from other users (source data) and adapts it to a new user's unique brain signals, outputting a more accurate and efficient decoder for BCI commands. This is primarily for BCI practitioners, researchers, and engineers working on improving BCI system efficiency.
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Use this if you need to quickly adapt a motor imagery-based BCI system to a new user without extensive, time-consuming calibration, leveraging brain signal data from other users.
Not ideal if your BCI application does not involve motor imagery or if you have ample time and resources for individual user calibration without needing to leverage existing data.
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Feb 06, 2022
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