Scalable selection of EEG features for compression

Telemedicine using information technology (IT) and communication networks is becoming common. Often, the medical doctor and the patient can discuss the problem by video teleconference and, if necessary, the patient's physiological data can be sent to the doctor. As part of this trend, we believe that brain waves can be used for telemedicine in the future. We expect that the diagnosis of remote patients will be realized by transferring electroencephalogram (EEG) data to a server or cloud. However, if EEG data are sent as they are, the data size will be significantly large. Thus, the compression of EEG data is desirable. Furthermore, should not affect the accuracy of diagnosis if data compression is performed. In this study, the relationship between the selected EEG signal features and the accuracy is investigated.