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.gitattributes

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+neural_data.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+tags:
+- brain-computer-interface
+- neural-decoding
+- motor-imagery
+- human-computer-interaction
+- neuralink
+task_categories:
+- brain-computer-interface
+task_ids:
+- motor-imagery
+- intent-decoding
+- visual-evoked-potentials
+- handwriting-recognition
+size_categories:
+- 10K<n<100K
+---
+
+# Dataset Card for BCI-FPS MOTOR_IMAGERY Dataset
+
+## Dataset Description
+
+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}
+}
+```

load_dataset.py

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+import json
+import pandas as pd
+from datasets import Dataset, DatasetDict
+
+def load_bci_fps_dataset(data_dir):
+    """
+    Load BCI-FPS dataset for Hugging Face.
+    
+    Args:
+        data_dir (str): Path to dataset directory
+    
+    Returns:
+        DatasetDict: Hugging Face dataset
+    """
+    # Load neural data
+    neural_data = []
+    with open(f"{data_dir}/neural_data.jsonl", 'r') as f:
+        for line in f:
+            if line.strip():
+                neural_data.append(json.loads(line))
+    
+    # Load intent stream
+    intent_stream = []
+    with open(f"{data_dir}/intent_stream.jsonl", 'r') as f:
+        for line in f:
+            if line.strip():
+                intent_stream.append(json.loads(line))
+    
+    # Create datasets
+    datasets = {
+        "neural_data": Dataset.from_list(neural_data),
+        "intent_stream": Dataset.from_list(intent_stream)
+    }
+    
+    # Load handwriting samples if exists
+    try:
+        with open(f"{data_dir}/handwriting_samples.json", 'r') as f:
+            handwriting = json.load(f)
+        datasets["handwriting"] = Dataset.from_list(handwriting)
+    except:
+        pass
+    
+    # Load metadata
+    with open(f"{data_dir}/metadata.json", 'r') as f:
+        metadata = json.load(f)
+    
+    dataset_dict = DatasetDict(datasets)
+    dataset_dict.info.metadata = metadata
+    
+    return dataset_dict
+
+# Example usage for Neuralink research
+if __name__ == "__main__":
+    dataset = load_bci_fps_dataset("./bci_data")
+    
+    print(f"Dataset keys: {list(dataset.keys())}")
+    print(f"Neural data samples: {len(dataset['neural_data'])}")
+    print(f"Intent stream samples: {len(dataset['intent_stream'])}")
+    
+    # Example: Extract motor imagery trials
+    motor_trials = [d for d in dataset['neural_data'] if d.get('type') == 'motor_imagery_trial']
+    print(f"Motor imagery trials: {len(motor_trials)}")

metadata.json

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+{
+  "dataset_info": {
+    "name": "BCI-FPS_MOTOR_IMAGERY_Dataset",
+    "description": "High-bandwidth neural training data for BCI research. Mode: motor_imagery",
+    "version": "1.0.0",
+    "license": "MIT",
+    "citation": "@misc{bci_fps_motor_imagery_2024,\n  title={BCI-FPS motor_imagery Training Dataset},\n  author={Neuralink Research},\n  year={2024},\n  note={High-frequency intent decoding data for brain-computer interface development}\n}"
+  },
+  "session_info": {
+    "session_id": "bci_fps_motor_imagery_1767171179245",
+    "mode": "motor_imagery",
+    "start_time": "2025-12-31T08:52:07.033Z",
+    "duration_ms": 52212,
+    "sampling_rate_hz": 1000,
+    "neural_channels": 32
+  },
+  "data_schema": {
+    "neural_data": {
+      "timestamp": "UNIX timestamp in milliseconds",
+      "session_time": "Time since session start in milliseconds",
+      "channels": "Object mapping channel names to neural signal values",
+      "intent_context": "Contextual information about user intent"
+    },
+    "intent_stream": {
+      "timestamp": "UNIX timestamp in milliseconds",
+      "mouse": "Mouse position and movement data",
+      "keyboard": "Keyboard state",
+      "camera": "Camera position and rotation",
+      "environment": "Game environment state"
+    },
+    "handwriting_samples": {
+      "letter": "Letter being traced",
+      "samples": "Array of handwriting samples with position and pressure data"
+    }
+  },
+  "research_applications": [
+    "Motor imagery decoding for prosthetic control",
+    "Simultaneous intent decoding for fluid BCI interfaces",
+    "Visual evoked potential (c-VEP) calibration",
+    "Handwriting intent recognition for text entry",
+    "Neural network training for brain-computer interfaces"
+  ],
+  "huggingface": {
+    "compatible": true,
+    "task_categories": [
+      "brain-computer-interface",
+      "neural-decoding",
+      "human-computer-interaction"
+    ],
+    "task_ids": [
+      "motor-imagery",
+      "intent-decoding",
+      "visual-evoked-potentials",
+      "handwriting-recognition"
+    ],
+    "language": [
+      "en"
+    ],
+    "size_categories": [
+      "10K<n<100K"
+    ]
+  }
+}

neural_data.jsonl

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+size 10683612