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README.md

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 ## Dataset Description
 
+*UNDER DEVELOPMENT*
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+Use Cases: BCI Intent Data Study and Testing (conceptual early design)
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+Training machine learning models for neural signal decoding without needing large real neural datasets, addressing data scarcity and privacy issues.
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+Augmenting real-world BCI data with synthetic samples to improve model robustness and diversity, as in GAN-based approaches.
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+Testing and calibrating BCI systems for motor imagery tasks like prosthetic control before human trials.
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+Simulating neural responses in assistive technologies for disabled individuals, enabling faster iteration in labs like Neuralink.
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+Developing predictive models for intent recognition in human-AI interactions and rehabilitative BCIs.
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+Enhancing clinical research datasets for disease risk assessment and patient outcome prediction in neuroengineering.
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+Validating algorithms in frontier labs (e.g., Neuralink, Paradromics) for high-data-rate implants by generating idealized signals.
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 This dataset contains high-bandwidth neural training data collected from BCI-FPS, a specialized training platform for brain-computer interface research.
 
 ### Dataset Summary