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metadata.json
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{
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"dataset_info": {
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"name": "BCI-FPS_MOTOR_IMAGERY_Dataset",
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"description": "High-bandwidth neural training data for BCI research. Mode: motor_imagery",
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"version": "1.0.0",
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"license": "MIT",
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"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}",
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"data_schema": {
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"neural_data": {
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"timestamp": "UNIX timestamp in milliseconds",
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"session_time": "Time since session start in milliseconds",
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"channels": "Object mapping channel names to neural signal values",
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"intent_context": "Contextual information about user intent"
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},
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"intent_stream": {
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"timestamp": "UNIX timestamp in milliseconds",
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"mouse": "Mouse position and movement data",
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"keyboard": "Keyboard state",
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"camera": "Camera position and rotation",
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"environment": "Game environment state"
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},
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"handwriting_samples": {
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"letter": "Letter being traced",
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"samples": "Array of handwriting samples with position and pressure data"
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}
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},
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"research_applications": [
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"Motor imagery decoding for prosthetic control",
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"Simultaneous intent decoding for fluid BCI interfaces",
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"Visual evoked potential (c-VEP) calibration",
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"Handwriting intent recognition for text entry",
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"Neural network training for brain-computer interfaces"
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]
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},
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"session_info": {
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"session_id": "bci_fps_motor_imagery_1767171179245",
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"mode": "motor_imagery",
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"start_time": "2025-12-31T08:52:07.033Z",
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"duration_ms": 52212,
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"sampling_rate_hz": 1000,
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"neural_channels": 32
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},
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"features": {
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"timestamp": "int64",
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"session_time": "int64",
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"mouse": {
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"position": "list<float64>",
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"delta": "list<int64>",
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"buttons": "int64"
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},
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"keyboard": {
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"mouse": "bool"
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},
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"camera": {
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"position": "list<float64>",
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"rotation": "list<float64>"
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},
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"environment": {
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"active_targets": [
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{
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"position": "list<float64>",
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"distance": "float64"
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}
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],
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"fps": "int64"
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},
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"type": "string",
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"button": "string",
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"target": "null",
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"neural_context": {
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"channel_0": "float64",
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"channel_1": "float64",
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"channel_2": "float64",
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"channel_3": "float64",
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"channel_4": "float64",
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"channel_5": "float64",
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"channel_6": "float64",
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"channel_7": "float64",
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"channel_8": "float64",
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"channel_9": "float64",
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"channel_10": "float64",
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"channel_11": "float64",
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"channel_12": "float64",
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"channel_13": "float64",
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"channel_14": "float64",
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"channel_15": "float64",
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"channel_16": "float64",
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"channel_17": "float64",
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"channel_18": "float64",
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"channel_19": "float64",
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"channel_20": "float64",
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"channel_21": "float64",
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"channel_22": "float64",
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"channel_23": "float64",
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"channel_24": "float64",
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"channel_25": "float64",
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"channel_26": "float64",
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"channel_27": "float64",
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"channel_28": "float64",
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"channel_29": "float64",
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"channel_30": "float64",
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"channel_31": "float64"
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}
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},
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"huggingface": {
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"compatible": true,
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"task_categories": [
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"brain-computer-interface",
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"neural-decoding",
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"human-computer-interaction"
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],
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"task_ids": [
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"motor-imagery",
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"intent-decoding",
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"visual-evoked-potentials",
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"handwriting-recognition"
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],
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"language": [
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"en"
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],
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"size_categories": [
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"10K<n<100K"
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]
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}
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}
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