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README.md

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+# DATASTRIKE BCI Timelapse Dataset
+
+## Description
+Time-synchronized multimodal dataset for BCI intent recognition, collected with frame-by-frame timelapse capture during FPS gameplay.
+
+## Key Features
+- **Frame-by-Frame Timelapse**: Synchronized image sequences at 320x240 resolution
+- **Multiple Capture Modes**: Manual (LMB), Auto-interval, and Sequence recording
+- **BCI Intent Labels**: 13 intent categories including timelapse capture events
+- **Time-Synced Game State**: Every frame includes synchronized game state data
+- **RLHF Data**: Automated ratings for combat events and capture actions
+- **Sequence Analysis**: Grouped frames into temporal sequences for time-series analysis
+
+## Dataset Structure
+- `timelapse_frames.jsonl`: Frame metadata (one per line)
+- `frames/`: JPG images for each frame
+- `sequences.json`: Temporal grouping of frames
+- `bci_intents.jsonl`: BCI intent transition history
+- `metadata.json`: Dataset statistics and configuration
+- `README.md`: This documentation
+
+## Capture Modes
+1. **MANUAL**: LMB click captures single frame (hold for burst)
+2. **AUTO**: Automatic capture at 500ms intervals
+3. **SEQUENCE**: Start/stop recording for continuous frame sequences
+
+## Dataset Statistics
+- **Total Frames**: 188
+- **Sequences**: 1
+- **Session Duration**: 01:35
+- **Player Level**: 1
+- **Accuracy**: 34%
+- **Total Kills**: 30
+
+## Frame Data Structure
+Each frame includes:
+- Image data (320x240 JPG)
+- Timestamp and game time
+- BCI intent label
+- Full game state (position, rotation, velocity, ammo, etc.)
+- Input data (mouse movements, keyboard state)
+- RLHF rating (if applicable)
+
+## Usage for BCI Research
+```python
+import json
+import cv2
+import numpy as np
+
+# Load frame metadata
+frames = []
+with open('timelapse_frames.jsonl', 'r') as f:
+    for line in f:
+        frames.append(json.loads(line))
+
+# Create time-series dataset
+X_images = []
+X_game_state = []
+y_intents = []
+
+for frame in frames:
+    # Load image
+    img_path = f"frames/{frame['image_filename'].split('/')[1]}"
+    img = cv2.imread(img_path)
+    X_images.append(img)
+    
+    # Game state features
+    game_state = frame['game_state']
+    features = [
+        game_state['player_position'][0],  # x
+        game_state['player_position'][2],  # z
+        game_state['combat_state']['ammo'],
+        game_state['game_stats']['level']
+    ]
+    X_game_state.append(features)
+    
+    # BCI intent label
+    y_intents.append(frame['bci_intent'])
+
+# For sequence modeling
+sequences = json.load(open('sequences.json', 'r'))
+for seq_id, sequence in sequences.items():
+    seq_frames = [f for f in frames if f['sequence_id'] == int(seq_id)]
+    # Process as temporal sequence for LSTM/Transformer models
+```
+
+## Citation
+```
+@dataset{datastrike_bci_timelapse_2026,
+  title={DATASTRIKE BCI Timelapse Dataset},
+  author={DATASTRIKE Team},
+  year={2026},
+  url={https://github.com/webxos/datastrike-bci},
+  note={Frame-by-frame timelapse capture for BCI intent recognition}
+}
+```
+
+Generated on 2026-01-07 by DATASTRIKE BCI Timelapse v4.0