Thought to Text

Update – April 2026

The Thought to Text project has wrapped up a strong semester. Key highlights:

• Data Collection App: Delivered several bug fixes to the adapted data collection application, improving stability and reliability across recording sessions.
• Canyon Undergraduate Research Conference: The team presented the Thought to Text project at the Canyon Undergraduate Research Conference. Joshua Peek, Justin Diaz Zapata, and Ryan Zdonek represented the project, sharing our progress on EEG-based text prediction and the EEGConformer architecture.
• Leadership Transition: With this update, we are proud to announce that Justin Diaz Zapata will be taking over as project lead going forward. Thank you to Joshua Peek and Ryan Zdonek for their foundational contributions to the project.

Project Overview – December 2025

The Thought to Text BCI project develops a brain-computer interface that directly translates EEG brain signals into text in real-time. Our goal is to help millions of individuals with severe motor disabilities — including those with ALS, cerebral palsy, and locked-in syndrome — who face significant communication barriers. Current assistive technologies average just 2–10 words per minute, far below the natural pace of human thought.

• Hardware: 16-channel OpenBCI Think Pulse system
• Architecture: EEGNet feature extraction + LSTM encoding
• Vocabulary: 30+ words for functional communication
• Performance target: Real-time inference under 100ms latency
• Signal flow: EEG Acquisition → Feature Extraction (EEGNet) → Temporal Encoding (LSTM) → Text Prediction

Modular Data Architecture

Our innovation centers on a unified inference engine operating across three distinct data sources — enabling rapid development and robust real-world deployment:

• ✓ Simulated Data (Complete): Synthetic EEG patterns with known ground-truth labels. Achieved 93–97% classification accuracy during initial development.
• ● Recorded Playback (Current Focus): Real EEG recordings played back at variable speeds (0.1x–10x), including labeled data for offline debugging.
• ⚙ Live Streaming (40% Complete): Real-time EEG acquisition with thread-safe buffered streaming, designed for sub-100ms continuous inference.

Key advantage: Train once on any data source — deploy everywhere without retraining.

Current Work: Recording Real Brain Signals

Playback System Operational:

• Recording pipeline with session management
• Variable-speed playback (0.1x – 10x)
• Label synchronization framework
• Offline evaluation and debugging tools
• Hardware-independent testing workflow

Hardware: OpenBCI Think Pulse 16-Channel Kit — wireless Bluetooth, 256Hz sampling rate, active electrodes for superior signal quality.

Currently recording team member EEG sessions to build a labeled dataset. Target: 100+ hours of diverse thought patterns capturing vocabulary variations and individual neural signatures.

Next Steps: Going Live (4–8 Weeks)

• Complete Live Data Pipeline: Finalize thread-safe streaming, implement real-time artifact rejection, and deploy adaptive filtering.
• Initial Live Testing Protocol: Conduct team member live inference sessions and benchmark end-to-end latency and classification accuracy.
• Data Collection Sprint: Record 50+ hours of labeled EEG across 5+ subjects, capturing diverse vocabulary patterns.
• Model Optimization: Fine-tune on real neural data, implement online learning, and develop subject-specific calibration.

Long-Term Vision (6 Months)

• Expand vocabulary to 50+ words for more comprehensive communication
• Develop user-facing GUI application for accessibility and ease of use
• Establish accessibility community testing partnerships
• Continue refining model performance with diverse user data
• Explore deployment pathways for individuals with communication disabilities