README.md

---
language:
- en
tags:
- reinforcement-learning
- neural-decoding
- motor-imagery
- human-computer-interaction
- neuralink
task_categories:
- reinforcement-learning
task_ids:
- natural-language-inference
- entity-linking-classification
- acceptability-classification
- intent-classification
size_categories:
- 10K<n<100K
---

# Dataset Card for BCI-FPS MOTOR_IMAGERY Dataset

## Dataset Description

*UNDER DEVELOPMENT*

*made using the /generator/ attached for testing*

Use Cases: BCI Intent Data Study and Testing (conceptual early design)

Training machine learning models for neural signal decoding without needing large real neural datasets, addressing data scarcity and privacy issues.

Augmenting real-world BCI data with synthetic samples to improve model robustness and diversity, as in GAN-based approaches.

Testing and calibrating BCI systems for motor imagery tasks like prosthetic control before human trials.

Simulating neural responses in assistive technologies for disabled individuals, enabling faster iteration in labs like Neuralink.

Developing predictive models for intent recognition in human-AI interactions and rehabilitative BCIs.

Enhancing clinical research datasets for disease risk assessment and patient outcome prediction in neuroengineering.

Validating algorithms in frontier labs (e.g., Neuralink, Paradromics) for high-data-rate implants by generating idealized signals.

This dataset contains high-bandwidth neural training data collected from BCI-FPS, a specialized training platform for brain-computer interface research.

### Dataset Summary

- **Training Mode**: MOTOR IMAGERY
- **Session ID**: bci_fps_motor_imagery_1767171179245
- **Duration**: 52 seconds
- **Sampling Rate**: 1000 Hz
- **Neural Channels**: 32
- **Data Points**: 11,314

### Supported Tasks

- **Motor Imagery Training for prosthetic control**
- **Neural Decoding**: Training models to decode user intent from neural signals
- **BCI Calibration**: Providing ground truth data for BCI system calibration
- **Disability Research**: Supporting development of assistive technologies

### Languages

English (interface and documentation)

## Dataset Structure

### Data Instances

```json
{
  "timestamp": 1767171127035,
  "session_time": 2,
  "channels": {
    "channel_0": 0.7145493839481488,
    "channel_1": 0.6894168445867142,
    "channel_2": 0.08142761930267149,
    "channel_3": -0.4847495027079371,
    "channel_4": -0.7151022782142631,
    "channel_5": -0.30725177077599913,
    "channel_6": 0.41521139153211245,
    "channel_7": 0.8975965762479154,
    "channel_8": 0.40940126876082966,
    "channel_9": -0.4091680578228324,
    "channel_10": -0.8292701881852992,
    "channel_11": -0.5904045145284711,
    "channel_12": 0.12196528544955941,
    "channel_13": 0.7040845591149026,
    "channel_14": 0.5296790688037042,
    "channel_15": 0.018181536760527098,
    "channel_16": -0.6973668262179662,
    "channel_17": -0.7437997196398959,
    "channel_18": -0.10714886215673841,
    "channel_19": 0.6246891444747351,
    "channel_20": 0.8560240877317689,
    "channel_21": 0.155749695078711,
    "channel_22": -0.4754514086663171,
    "channel_23": -0.7632646743624881,
    "channel_24": -0.42658424045199833,
    "channel_25": 0.47380668620054267,
    "channel_26": 0.7558851981047924,
    "channel_27": 0.5145527444334146,
    "channel_28": -0.22899647502709344,
    "channel_29": -0.8498710316208474,
    "channel_30": -0.5816021940073672,
    "channel_31": 0.2096020563849897
  },
  "intent_context": {
    "mouse_movement": [
      0,
      0
    ],
    "keyboard_state": {
      "mouse": false
    },
    "camera_rotation": [
      0,
      0,
      0
    ],
    "active_targets": 0
  }
}
```

### Data Fields

See `metadata.json` for complete schema documentation.

## Dataset Creation

### Source Data

- **Platform**: Web-based BCI-FPS Training Environment
- **Sampling Rate**: 1000 Hz
- **Collection Method**: Real-time telemetry during BCI training tasks
- **Neural Simulation**: Synthetic neural data representing ideal BCI signals

### Annotations

- **Annotation process**: Automatic intent labeling during gameplay
- **Annotation types**: Motor imagery, visual stimuli, handwriting intent
- **Who annotated**: System automatically labels based on game state

### Personal and Sensitive Information

No personal information is collected. All data is synthetic/anonymous.

## Considerations for Using the Data

### Social Impact

This dataset enables research in:
- Neuralink-style brain-computer interfaces
- Assistive technologies for disabled individuals
- Human-AI interaction systems
- Neural decoding algorithms

### Discussion of Biases

Synthetic neural data may not perfectly represent biological signals. Results should be validated with real neural recordings.

### Other Known Limitations

- Simulated neural signals
- Idealized game environment
- Limited to specific training tasks

## Additional Information

### Dataset Curators

BCI-FPS Research Team

### Licensing Information

MIT License

### Citation Information

```bibtex
@misc{bci_fps_motor_imagery_2024,
  title={BCI-FPS motor_imagery Training Dataset},
  author={Neuralink Research},
  year={2024},
  note={High-frequency intent decoding data for brain-computer interface development}
}
```