Group member names (6 members):
- Syed Reza Ali Abdi
- Logan Hindley
- Omar Mohamed
- William Baird
- Kenneth Joseph
- Muhammad Talha
Staying focused while studying is hard—especially when distractions are constant and invisible. We wanted to explore whether real-time EEG data could be used to measure focus and cognitive engagement, and then turn that invisible signal into something tangible and useful.
That idea became StudyWave: a project that transforms brainwaves into meaningful feedback to help users understand and improve their concentration.
StudyWave uses EEG data from a Muse headset to analyze brain activity and estimate a user’s focus level while studying.
The system:
- Streams live EEG data
- Filters and processes signals
- Extracts meaningful features
- Identifies periods of steady focus
- Visualizes results for interpretation and experimentation
The goal is not diagnosis, but awareness—helping users see when they are focused and when their attention drifts.
- EEG Data Collection EEG signals are streamed from a Muse headset using BrainFlow-compatible tooling.
- Signal Processing
- Noise filtering
- Segmentation into time windows
- Identification of steady vs. fluctuating brainwave patterns
- Analysis & Experimentation Jupyter notebooks are used to test models, analyze trends, and visualize EEG signals associated with focus.
- Visualization Graphs and prototype UI concepts show how real-time feedback could be delivered to users.
- Python
- Jupyter Notebooks
- BrainFlow
- NumPy / Pandas
- Matplotlib
- Muse EEG Headset
- CSV-based signal analysis
- Python 3.x
- Jupyter Notebook
- BrainFlow
- Muse EEG headset (or sample CSV data provided)
pip install brainflow numpy pandas matplotlib jupyter- Launch Jupyter Notebook:
jupyter notebook- Open one of the notebooks in src/ or src2/
- Run cells sequentially to:
- Load EEG data
- Process signals
- Visualize focus-related patterns
If you don’t have access to a Muse headset, you can still experiment using:
- steady_segments.csv
- eeg_data_test.csv
These files contain EEG samples used during development and testing.
- Filtering noisy EEG data
- Defining what “focus” means quantitatively
- Working with real-time biosignals under hackathon time constraints
- Interpreting EEG patterns responsibly
- Successfully streaming and processing EEG data
- Identifying steady-focus segments
- Building a reproducible analysis pipeline
- Creating a strong foundation for real-time neurofeedback
- EEG data is powerful but noisy
- Signal processing matters as much as machine learning
- Even simple metrics can provide meaningful insights
- Rapid prototyping is essential in neurotech
- Real-time focus scoring
- Improved signal classification models
- A fully interactive frontend
- Personalized baselines per user
- Integration with study tools (Pomodoro timers, productivity apps)
👉 Project Page: https://devpost.com/software/wedidathink
Built with curiosity, caffeine, and brainwaves at NatHacks 🧠⚡
MUSE USAGE VIDEO: https://www.youtube.com/watch?v=omn7y3TIsGc MUSE SDK INSTALLER: https://drive.google.com/drive/folders/1ID35qK7zCvRXmQTFsbDgmPkVGhnPeCxa?usp=sharing https://portal.neuralberta.tech/course/3/md/55 https://www.youtube.com/watch?v=Qdwyhi2ulZU <--- Useful brainflow video.