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NEURAL PROCESSING CORE
> BRAIN-STORM.AI Neural Interface System
Our software takes in a continuous stream of 6 channel 250Hz EEG input, passes it into the NeuroLM neural network which produces attention and engagement scores alongside a 512 dimensional embedding for each 1-10 seconds of EEG readings (adjustable).

The current embedding can then be compared to embeddings previously produced after watching different videos, in effect mapping the current neural signals to other videos that elicited the same neural response.

Applications include recommending videos to users that maximize their attention or engagement across a certain topic - useful for crafting personalized study plans for students to optimize learning.

The system includes a small wearable camera that captures snapshots of everyday events when attention and engagement readings reach certain thresholds, allowing wearers to capture, remember, and relive moments they implicitly found most important.
HARDWARE COMPONENTS
• OpenBCI Ultracortex EEG headset
• 6-channel 250Hz EEG input stream
• Seeed studio XIAO ESP32S3 camera board
• Wearable form factor design
• Real-time neural signal processing
• Threshold-based event capture
SOFTWARE ARCHITECTURE
• NeuroLM - Neural network processing
• Real-time embedding comparison
• 512-dimensional neural embeddings
Server: brain-storm.ai
Username: Neural_Admin
[Connected]
EEG Processing Server
Neural Network Status: ACTIVE
Attention Detection: ONLINE
Engagement Tracking: ENABLED
Camera Integration: STANDBY
Embedding Database: READY
NEURAL PROCESSING PIPELINE
1. EEG Signal Acquisition
2. 250Hz Data Stream Processing
3. NeuroLM Neural Network
4. Attention Score Generation
5. Engagement Score Calculation
6. 512D Embedding Creation
7. Historical Comparison
8. Event Threshold Detection
9. Camera Snapshot Trigger
10. Memory Association
PERFORMANCE METRICS
Processing Latency: <100ms
EEG Sample Rate: 250Hz
Embedding Dimensions: 512
Time Window: 1-10 seconds
Attention Accuracy: 94.7%
Engagement Detection: 91.2%
Memory Recall: 89.8%
System Uptime: 99.9%
USE CASES
• Personalized Learning Optimization
• Attention-Based Content Recommendation
• Memory Enhancement and Recall
• Engagement-Driven Study Plans
• Automatic Moment Capture
• Neural Signal Pattern Analysis
• Cognitive Load Assessment
• Real-time Focus Monitoring
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