| --- |
| license: mit |
| task_categories: |
| - image-classification |
| - tabular-regression |
| - tabular-classification |
| - reinforcement-learning |
| - robotics |
| - image-segmentation |
| - image-to-image |
| - image-feature-extraction |
| tags: |
| - bci |
| - brain-computer-interface |
| - neuroscience |
| - gaming |
| - fps |
| - RLHF |
| - signal-processing |
| - motor-imagery |
| - A11Y |
| - WCAG |
| --- |
| |
| [](https://webxos.netlify.app) |
| [](https://github.com/webxos/webxos) |
| [](https://huggingface.co/webxos) |
| [](https://x.com/webxos) |
|
|
| <div style=" |
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| "> |
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| ________ _____________________ ______________________________.___ ____ __.___________ |
| \______ \ / _ \__ ___/ _ \ / _____/\__ ___/\______ \ | |/ _|\_ _____/ |
| | | \ / /_\ \| | / /_\ \ \_____ \ | | | _/ | < | __)_ |
| | ` \/ | \ |/ | \/ \ | | | | \ | | \ | \ |
| /_______ /\____|__ /____|\____|__ /_______ / |____| |____|_ /___|____|__ \/_______ / |
| \/ \/ \/ \/ \/ \/ \/ |
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| </div> |
| |
| # DATASTRIKE RLHF for Synthetic BCI Timelapse Dataset |
| |
| *UNDER DEVELOPMENT* |
|
|
| *This dataset was created with the DATASTRIKE app located in the /gym/ folder. Download the gym to train your own similar datasets* |
|
|
| ## Description |
|
|
| *Simulated data for Intent testing, does not use real Neuralink/BCI hardware signals.* |
|
|
| Time-synchronized multimodal dataset for BCI intent recognition, collected with frame-by-frame timelapse capture during FPS gameplay. |
| Hosted on Hugging Face for brain-computer interface (BCI) intent ecognition research. It was collected via frame-by-frame timelapse |
| capture during first-person shooter (FPS) gameplay and includes synchronized image sequences (320x240 JPGs), game state data (like |
| player position, velocity, ammo, and combat stats), BCI intent labels across 13 categories, input data (mouse/keyboard), and RLHF ratings |
| for combat and capture actions. The dataset is small, with 188 total frames grouped into 1 temporal sequence from a 1:35-minute session, |
| 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 |
| file for time-series analysis, making it suitable for deep learning models like LSTMs or Transformers on multimodal temporal data. Tags |
| include BCI, timelapse, FPS gameplay, intent recognition, multimodal, time-series, RLHF, and sequence modeling. |
|
|
| This BCI Intent Data Study (conceptual early design) is for training machine learning models for neural signal decoding without needing |
| large scale real hardware BCI datasets, addressing data scarcity and privacy issues around BCI intent studies. |
|
|
| ## 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 |
| ``` |
|
|
| ## License |
|
|
| MIT |
|
|
| Generated on 2026-01-07 by DATASTRIKE by webXOS |
