دانلود رایگان مقاله انگلیسی طبقه بندی محاسبات ذهنی و حالت استراحت بر اساس الکتروانسفالوگرافی گوش - IEEE 2018

Abstract

Electroencephalography (EEG) has been mainly utilized for developing brain-computer interface (BCI) systems. In recent, use of Ear-EEG measured around the ears has been proposed to enhance the practicality of conventional EEG-based BCI systems. Most of BCI systems based on Ear-EEG have used exogenous BCI paradigms employing external stimuli. In this study, we investigated the feasibility of using Ear-EEG in developing an endogenous BCI system that uses self-modulated brain signals. EEG data was measured while subjects performed mental arithmetic (MA) and baseline (BL) task. EEG data analysis was performed after dividing the whole brain area into four regions of interest (frontal, central, occipital, and ear area) to compare their EEG characteristics and classification performance. Similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs, and classification performance was insignificant between them, except occipital area (frontal: 72.6 %, central: 76.7 %, occipital: 82.6 % and ear: 75.6 %). From the results, we could confirm the possibility of using Ear-EEG for developing an endogenous BCI system.

IV. CONCLUSION

This study aims to develop an Ear-EEG based endogenous BCI system by discriminating brain responses induced by MA and BL. Brain responses (ERD/ERS) measured around the ears were similar to those measured from the other brain areas. In particular, strong ERS pattern in alpha frequency band was observed across all ROIs, which is consistent with a previous study that reported alpha activity increase during high cognitive task [14]. The mean classification accuracy using Ear-EEG was 75.6 %, and it was not significantly difference between other brain areas, except occipital area. From the analysis results, we confirmed the feasibility of using endogenous paradigm for developing an Ear-EEG based BCI systems, but more experiments should be performed with more subjects to generalize our results.