Dr. Theerawit Wilaiprasitporn has been invited to be part of organizing committees for the International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2018, Bangkok, Thailand
Dr. Theerawit Wilaiprasitporn has been invited to be Tutorial/Workshop Activity Chair for ITC-CSCC 2018. He will organize one tutorial session named "Brain-Controlled Robot".
Brain-computer interface (BCI) has been active research issue for over three centuries. Dramatically improving on electronic components and computational resources makes BCI very fascinating to researchers at this moment. BCI is served as non-muscular communication pathway between man and machine using brain signal or response, especially for motor disabled patients.
Amyotrophic lateral sclerosis (ALS) is an example of such a disease. A BCI researcher is major focusing on three types of brain responses which are Event-related potential (ERP), Steady state visual evoked potential (SSVEP) and Motor Imagery (MI). ERP and SSVEP usually generate by stimulating human sensory system such as visual stimulation, auditory stimulation, tactile stimulation.
To generate MI signals, a man has to imagine that he is using his motor function (hand and/or foot movements) but without actually performing movement.
There are various measurement types for recording ERP, SSVEP, and MI brain responses in BCI research. Electroencephalography (EEG) is the most famous measurement technique because it is non-invasive and lower cost compared to the others. To obtain brain responses, EEG is used to measure variation of electrical potential signals across the scalp.
Then various signal processing algorithms are employed to analyze target brain responses from measured potential signals. Electrical potential source comes from billions of neurons inside the brain.
In this workshop, we first introduce principle concept of EEG measurement system using low cost and open source amplifier named OpenBCI. Then we address SSVEP stimulation and its response from EEG recording. Finally, we demonstrate simple SSVEP-based BCI system for online brain-controlled robotic toy.