Eyes-closed hybrid brain-computer interface employing frontal brain activation

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroenceph-alography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA-from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 +/- 10.3%) was shown to be significantly higher than those of unimodal EEG-based (77.3 +/- 15.9%) and NIRS-based BCI (75.9 +/- 6.3%). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451) and by National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1A6A3A01003543) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning) (No. 2017R1C1B5017909).This work was supported by a grant from Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science, ICT & Future Planning) (No.2017R1C1B5017909). Correspondence to KRM and HJH

양성전분을 이용한 인쇄용지의 표면사이징에 관한 연구

학위논문(석사)--서울대학교 대학원 :임산공학과,2000.Maste

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 +/- 7.1/85.5 +/- 8.1% and 85.8 +/- 8.6/79.5 +/- 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.The study was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451). The study was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037032) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2017R1C1B5017909). Correspondence to K.-R.M. and H.-J.H

Ternary Near-Infrared Spectroscopy Brain-Computer Interface With Increased Information Transfer Rate Using Prefrontal Hemodynamic Changes During Mental Arithmetic, Breath-Holding, and Idle State

The implementation of a multi-class brain-computer interface (BCI) is an efficient way to increase the information transfer rate (ITR) generally expressed in a unit of bits/trial. The improvement of ITR is of specific importance for near-infrared spectroscopy (NIRS)-BCI, because brain hemodynamic responses recorded by NIRS are much slower than the electrophysiological responses of the brain. In this paper, to implement a ternary NIRS-BCI with increased ITR, we used prefrontal cortex (PFC) hemodynamic changes induced by breath-holding (i.e., the voluntary suppression of breathing movements), which have never been used in the field of BCIs. Additionally, we used traditional BCI tasks such as mental arithmetic and idle state to implement ternary NIRS-BCI. As a result, an average offline ternary classification accuracy of 72.6 +/- 10.7% could be achieved, which is the best performance of NIRS-BCI based on PFC hemodynamic changes reported to date. Because the number of available input commands was increased and the classification accuracy remained at an acceptable level, the ITR of the ternary BCI (0.51 +/- 0.29 bits/trial) was 1.6 times higher than that of the traditional binary BCI (0.31 +/- 0.21 bits/trial). Although the hemodynamic changes induced by the breath-holding were not caused by mental state changes, breath-holding proved to be a promising hybrid BCI task for implementing more efficient ternary NIRS-BCI.This work was supported in part by the ICT Research and Development Program of MSIT/IITP, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought through AR/VR interface, under Grant 2017-0-00432, and in part by the Brain Research Program through the National Research Foundation of Korea, Ministry of Science and ICT, under Grant NRF-2015M3C7A1031969 and Grant NRF-2017R1A6A3A01003543

수안보-연풍지역에서의 조선누층군과 옥천누층군의 접촉관계 및 구조특성의 비교 연구

학위논문(석사)--서울대학교 대학원 :지구환경과학부,2001.Maste