{
  "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}",
    "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"
    ]
  },
  "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
  },
  "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"
    ]
  }
}