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This is a Capstone Project from the Department of Biomedical Engineering at the University of Houston. The members in this group were Faheem Ershad, Ethan Hart, Zhihe (Harrison) Zhao, and Kaitlyn Robinson.

The objective of this project is to create a working brain-computer interface (BCI) that combines a dry-electrode EEG headset and online/offline classifier algorithms to control a computer cursor. The classifier algorithms will extract relevant features from the EEG data using PSD (Power Spectral Density) and CSP (Common Spatial Patterns) and then classify with SVM (Support Vector Machines). An overview of the final BCI system protocol is shown below.

The repository contains the following branches with their respective descriptions below.

Main branches

• Data
• Two Binary SVM Classifers (offline use only)
• Online Training and Prediction
• Cursor Control

Supplementary branches

• Archives and Executables
• Streaming via LSL
• Validation
• Streaming to MATLAB directly from Serial Port