The main goal of this project is to utilize a commercially available OpenBCI Ultracortex IV for the measurement of Electroencephalogram(EEG) signals. A pipeline consisting of preprocessing, classification and extraction is employed to transform the motor execution EEG signal into a singular Left or Right output. This output is then further displayed on an Interface that offers the option to either calibrate or play a simple game.

The Ultracortex and relevant software were used to determine the sensor layout, with the placement of the sensors focused on areas which exhibited high cortical activity during motor execution. Experiments were strategically designed to optimize our chance of successful readings and OpenVIBE was used in conjecture with preprocessing filters to save the raw and filtered data which was further sent to the Machine Learning group.

The collected data was analyzed through Spectrograms, Power Spectral Density(PSD) and Event-Related Desynchronization/Synchronization(ERDS) plots. The analysis aimed to confirm whether the desired activity occurred and whether the observed patterns resemble those documented in other research papers.

The data from the headset is live-streamed to the interface via Lab Streaming Layer(LSL) where it undergoes further filtering before being sent to the Machine learning group. This process was done through python libraries which then allowed for efficient and effective communication between the other groups.