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--- |
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language: |
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- en |
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tags: |
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- reinforcement-learning |
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- neural-decoding |
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- motor-imagery |
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- human-computer-interaction |
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- neuralink |
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- bci |
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- fps |
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- humancomputerinteraction |
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- motorimagery |
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- neuraldecoding |
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task_categories: |
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- reinforcement-learning |
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- time-series-forecasting |
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- tabular-classification |
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task_ids: |
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- natural-language-inference |
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- entity-linking-classification |
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- acceptability-classification |
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- intent-classification |
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size_categories: |
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- 10K<n<100K |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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# Dataset Card for BCI-FPS MOTOR_IMAGERY Dataset |
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## Dataset Description |
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*UNDER DEVELOPMENT for TESTING* |
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*This dataset was made using app in the /generator/ folder which is under development testing.* |
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## Use case ideas and concepts: |
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*Simulated data for Intent testing, does not use real Neuralink/BCI hardware signals.* |
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BCI Intent Data Study and Testing (conceptual early design) for training machine learning models for neural signal |
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decoding without needing large scale real hardware BCI datasets, addressing data scarcity and privacy issues around BCI intent studies. |
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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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## Dataset Summary |
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- **Training Mode**: MOTOR IMAGERY |
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- **Session ID**: bci_fps_motor_imagery_1767171179245 |
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- **Duration**: 52 seconds |
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- **Sampling Rate**: 1000 Hz |
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- **Neural Channels**: 32 |
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- **Data Points**: 11,314 |
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## Supported Tasks |
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- **Motor Imagery Training for prosthetic control** |
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- **Neural Decoding**: Training models to decode user intent from neural signals |
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- **BCI Calibration**: Providing ground truth data for BCI system calibration |
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- **Disability Research**: Supporting development of assistive technologies |
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## Languages |
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English (interface and documentation) |
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## Data Instances |
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```json |
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{ |
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"timestamp": 1767171127035, |
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"session_time": 2, |
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"channels": { |
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"channel_0": 0.7145493839481488, |
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"channel_1": 0.6894168445867142, |
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"channel_2": 0.08142761930267149, |
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"channel_3": -0.4847495027079371, |
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"channel_4": -0.7151022782142631, |
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"channel_5": -0.30725177077599913, |
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"channel_6": 0.41521139153211245, |
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"channel_7": 0.8975965762479154, |
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"channel_8": 0.40940126876082966, |
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"channel_9": -0.4091680578228324, |
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"channel_10": -0.8292701881852992, |
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"channel_11": -0.5904045145284711, |
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"channel_12": 0.12196528544955941, |
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"channel_13": 0.7040845591149026, |
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"channel_14": 0.5296790688037042, |
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"channel_15": 0.018181536760527098, |
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"channel_16": -0.6973668262179662, |
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"channel_17": -0.7437997196398959, |
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"channel_18": -0.10714886215673841, |
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"channel_19": 0.6246891444747351, |
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"channel_20": 0.8560240877317689, |
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"channel_21": 0.155749695078711, |
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"channel_22": -0.4754514086663171, |
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"channel_23": -0.7632646743624881, |
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"channel_24": -0.42658424045199833, |
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"channel_25": 0.47380668620054267, |
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"channel_26": 0.7558851981047924, |
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"channel_27": 0.5145527444334146, |
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"channel_28": -0.22899647502709344, |
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"channel_29": -0.8498710316208474, |
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"channel_30": -0.5816021940073672, |
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"channel_31": 0.2096020563849897 |
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}, |
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"intent_context": { |
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"mouse_movement": [ |
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0, |
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0 |
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], |
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"keyboard_state": { |
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"mouse": false |
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}, |
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"camera_rotation": [ |
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0, |
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0, |
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0 |
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], |
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"active_targets": 0 |
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} |
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} |
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``` |
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## Data Fields |
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See `metadata.json` for complete schema documentation. |
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## Source Data |
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- **Platform**: Web-based BCI-FPS Training Environment |
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- **Sampling Rate**: 1000 Hz |
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- **Collection Method**: Real-time telemetry during BCI training tasks |
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- **Neural Simulation**: Synthetic neural data representing ideal BCI signals |
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## Annotations |
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- **Annotation process**: Automatic intent labeling during gameplay |
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- **Annotation types**: Motor imagery, visual stimuli, handwriting intent |
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- **Who annotated**: System automatically labels based on game state |
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## Personal and Sensitive Information |
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No personal information is collected. All data is synthetic/anonymous. |
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## Social Impact |
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This dataset enables research in: |
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- Neuralink-style brain-computer interfaces |
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- Assistive technologies for disabled individuals |
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- Human-AI interaction systems |
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- Neural decoding algorithms |
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## Discussion of Biases |
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Synthetic neural data may not perfectly represent biological signals. Results should be validated with real neural recordings. |
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## Other Known Limitations |
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- Simulated neural signals |
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- Idealized game environment |
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- Limited to specific training tasks |
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## Additional Information |
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## Dataset Curators |
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huggingface.co/webXOS |
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### Licensing Information |
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MIT License |
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## Citation Information |
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```bibtex |
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@misc{bci_fps_motor_imagery, |
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title={BCI-FPS motor_imagery Dataset}, |
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author={webXOS, |
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year={2024}, |
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note={for testing and development purposes} |
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} |
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``` |
