Classification of EEG Signals of Natural Thoughts

Abstract

In the early stage of BMI research, studies attempted to classify natural thoughts such as “ I want to go left ” or “ I want to go right ” based on EEG signals, but the classification accuracy did not improve. Consequently, to enhance accuracy, more classifiable yet unnatural thought patterns were employed. However, controlling BMI with unnatural thoughts is challenging. Moreover, current EEG measurement devices and analytical methods have progressed since the early BMI research days. Therefore, this study aims to verify the feasibility of bidirectional classification based on natural thoughts using various analytical methods, including deep learning techniques.