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--- |
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license: mit |
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task_categories: |
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- image-classification |
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- tabular-regression |
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- tabular-classification |
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- reinforcement-learning |
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- robotics |
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- image-segmentation |
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- image-to-image |
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- image-feature-extraction |
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tags: |
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- bci |
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- brain-computer-interface |
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- neuroscience |
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- gaming |
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- fps |
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- RLHF |
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- signal-processing |
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- motor-imagery |
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- A11Y |
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- WCAG |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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<div style=" |
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background: #00FF00; |
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border-left: 4px solid #00FF00; |
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padding: 1.5rem; |
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margin: 2rem 0; |
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font-family: 'Fira Code', 'Courier New', monospace; |
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color: #00FF00; |
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border-radius: 0 8px 8px 0; |
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"> |
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<pre style=" |
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font-size: 8px; |
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line-height: 1.2; |
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margin: 0; |
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overflow-x: auto; |
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color: #00FF00; |
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"> |
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________ _____________________ ______________________________.___ ____ __.___________ |
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\______ \ / _ \__ ___/ _ \ / _____/\__ ___/\______ \ | |/ _|\_ _____/ |
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| | \ / /_\ \| | / /_\ \ \_____ \ | | | _/ | < | __)_ |
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| ` \/ | \ |/ | \/ \ | | | | \ | | \ | \ |
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/_______ /\____|__ /____|\____|__ /_______ / |____| |____|_ /___|____|__ \/_______ / |
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\/ \/ \/ \/ \/ \/ \/ |
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</div> |
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# DATASTRIKE BCI Timelapse Dataset |
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*UNDER DEVELOPMENT* |
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*This dataset was created with the DATASTRIKE app located in the /gym/ folder. Download the gym to train your own similar datasets* |
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## Description |
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*Simulated data for Intent testing, does not use real Neuralink/BCI hardware signals.* |
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Time-synchronized multimodal dataset for BCI intent recognition, collected with frame-by-frame timelapse capture during FPS gameplay. |
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Hosted on Hugging Face for brain-computer interface (BCI) intent ecognition research. It was collected via frame-by-frame timelapse |
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capture during first-person shooter (FPS) gameplay and includes synchronized image sequences (320x240 JPGs), game state data (like |
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player position, velocity, ammo, and combat stats), BCI intent labels across 13 categories, input data (mouse/keyboard), and RLHF ratings |
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for combat and capture actions. The dataset is small, with 188 total frames grouped into 1 temporal sequence from a 1:35-minute session, |
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and a download size of about 685 kB. It's structured with JSONL files for metadata and intents, a directory of images, and a sequences.json |
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file for time-series analysis, making it suitable for deep learning models like LSTMs or Transformers on multimodal temporal data. Tags |
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include BCI, timelapse, FPS gameplay, intent recognition, multimodal, time-series, RLHF, and sequence modeling. |
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This BCI Intent Data Study (conceptual early design) is for training machine learning models for neural signal decoding without needing |
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large scale real hardware BCI datasets, addressing data scarcity and privacy issues around BCI intent studies. |
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## Key Features |
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- **Frame-by-Frame Timelapse**: Synchronized image sequences at 320x240 resolution |
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- **Multiple Capture Modes**: Manual (LMB), Auto-interval, and Sequence recording |
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- **BCI Intent Labels**: 13 intent categories including timelapse capture events |
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- **Time-Synced Game State**: Every frame includes synchronized game state data |
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- **RLHF Data**: Automated ratings for combat events and capture actions |
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- **Sequence Analysis**: Grouped frames into temporal sequences for time-series analysis |
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## Dataset Structure |
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- `timelapse_frames.jsonl`: Frame metadata (one per line) |
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- `frames/`: JPG images for each frame |
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- `sequences.json`: Temporal grouping of frames |
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- `bci_intents.jsonl`: BCI intent transition history |
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- `metadata.json`: Dataset statistics and configuration |
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- `README.md`: This documentation |
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## Capture Modes |
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1. **MANUAL**: LMB click captures single frame (hold for burst) |
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2. **AUTO**: Automatic capture at 500ms intervals |
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3. **SEQUENCE**: Start/stop recording for continuous frame sequences |
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## Dataset Statistics |
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- **Total Frames**: 188 |
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- **Sequences**: 1 |
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- **Session Duration**: 01:35 |
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- **Player Level**: 1 |
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- **Accuracy**: 34% |
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- **Total Kills**: 30 |
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## Frame Data Structure |
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Each frame includes: |
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- Image data (320x240 JPG) |
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- Timestamp and game time |
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- BCI intent label |
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- Full game state (position, rotation, velocity, ammo, etc.) |
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- Input data (mouse movements, keyboard state) |
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- RLHF rating (if applicable) |
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## Usage for BCI Research |
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```python |
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import json |
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import cv2 |
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import numpy as np |
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# Load frame metadata |
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frames = [] |
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with open('timelapse_frames.jsonl', 'r') as f: |
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for line in f: |
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frames.append(json.loads(line)) |
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# Create time-series dataset |
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X_images = [] |
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X_game_state = [] |
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y_intents = [] |
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for frame in frames: |
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# Load image |
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img_path = f"frames/{frame['image_filename'].split('/')[1]}" |
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img = cv2.imread(img_path) |
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X_images.append(img) |
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# Game state features |
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game_state = frame['game_state'] |
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features = [ |
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game_state['player_position'][0], # x |
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game_state['player_position'][2], # z |
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game_state['combat_state']['ammo'], |
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game_state['game_stats']['level'] |
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] |
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X_game_state.append(features) |
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# BCI intent label |
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y_intents.append(frame['bci_intent']) |
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# For sequence modeling |
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sequences = json.load(open('sequences.json', 'r')) |
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for seq_id, sequence in sequences.items(): |
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seq_frames = [f for f in frames if f['sequence_id'] == int(seq_id)] |
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# Process as temporal sequence for LSTM/Transformer models |
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``` |
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## Citation |
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``` |
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@dataset{datastrike_bci, |
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title={DATASTRIKE BCI Timelapse Dataset}, |
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author={webXOS}, |
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year={2026}, |
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url={https://github.com/webxos}, |
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note={Frame-by-frame timelapse capture for BCI intent recognition} |
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} |
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``` |
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Generated on 2026-01-07 by DATASTRIKE by webXOS |
