Datasets:
File size: 2,187 Bytes
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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}",
"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"
]
}
} |