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data.jsonl

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+[
+  {
+    "timestamp": 1767171179245,
+    "session_time": 0,
+    "mouse": {
+      "position": [0.0, 0.0],
+      "delta": [0, 0],
+      "buttons": 0
+    },
+    "keyboard": {
+      "mouse": false
+    },
+    "camera": {
+      "position": [0.0, 0.0, 0.0],
+      "rotation": [0.0, 0.0, 0.0]
+    },
+    "environment": {
+      "active_targets": [
+        {
+          "position": [0.0, 0.0, 0.0],
+          "distance": 0.0
+        }
+      ],
+      "fps": 60
+    },
+    "type": "mouse_move",
+    "button": null,
+    "target": null,
+    "neural_context": {
+      "channel_0": 0.0,
+      "channel_1": 0.0,
+      "channel_2": 0.0,
+      "channel_3": 0.0,
+      "channel_4": 0.0,
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+    }
+  }
+]

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}",
+    "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"
+    ]
+  }
+}