As part of the 2021 NeurotechX Open Challenge, we are developing a brain-computer interface which uses machine learning to discriminate between endogenous and exogenous attention based on beta, alpha, and theta activity. Such a product can be applied in a variety of different roles or contexts such as mindfulness and meditation and education.
Data Collection
We are utilizing Emotiv’s EPOC+ EEG headset to record brain activity of subjects performing a variety of neuropsychological tests such as the Stroop and Posner cueing tasks.
Software
We are trying to utilize advanced machine learning techniques such as transfer and deep learning to build a classifier to distinguish between different attentional states.