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OpenBCI-Controlled Autonomous Wheelchair

Motor Imagery controlled wheelchair. For detailed methodology, see the MILO (Motor Imagery LocoMotive) video and Github.
This month we are giving a shout-out to McGill NeuroTech for their inspirational project titled Milo. It was submitted to the NeuroTechX Student Clubs Competition – Open Challenge 2019!

The group accomplished the following in less than two months:
1. used OpenBCI Cyton and Gold Cup Electrodes to measure EEG data from sensory motor cortex and collect motor imagery data set, as well as eye blinks and jaw artifacts
2. applied OpenBCI GUI’s built-in notch filter and bandpass filter to “clean” raw data
3. utilized spectral features and custom signal processing methods to convert raw data to usable information
4. created the fully brain-controlled wheelchair Milo that is accurate, maneuverable, and easy-to-use

We are thrilled to see the interdisciplinary collaboration of students working in EEG, software, and mechanical engineering! 

The work of McGill Neurotech and other students speaks to our mission to make neurotechnology and biosensing more accessible. We strive to lower the barrier to entry and support students and researchers all over the world. 

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