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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Komarov, OleksiiSubject: search.filters.subject.electroencephalography

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    Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students
    (2020-04-01)
    Komarov, Oleksii
    ;
    Ko, Li-Wei
    ;
    Jung, Tzyy-Ping
    ;
    交大名義發表
    ;
    生物資訊及系統生物研究所
    University students are routinely influenced by a variety of natural stressors and experience irregular sleep-wake cycles caused by the necessity to trade sleep for studying while dealing with academic assignments. Often these factors result in long-term issues with daytime sleepiness, emotional instability, and mental exhaustion, which may lead to difficulties in the educational process. This study introduces the Daily Sampling System (DSS) implemented as a smartphone application, which combines a set of self-assessment scales for evaluating variations in the emotional state and sleep quality throughout a full academic term. In addition to submitting the daily sampling scores, the participants regularly filled in the Depression, Anxiety, and Stress Scales (DASS) reports and took part in resting-state EEG data recording immediately after report completion. In total, this study collected 1835 daily samples and 94 combined DASS with EEG datasets from 18 university students (aged 23-27 years), with 79.3 +/- 15.3% response ratio in submitting the daily reports during an academic semester. The results of pairwise testing and multiple regression analysis demonstrate that the daily level of self-perceived fatigue correlates positively with stress, daytime sleepiness, and negatively with alertness on awakening, self-evaluated sleep quality, and sleep duration. The spectral analysis of the EEG data reveals a significant increase in the resting-state spectral power density across the theta and low-alpha frequency bands associated with increased levels of anxiety and stress. Additionally, the state of depression was accompanied by an intensification of high-frequency EEG activity over the temporal regions. No significant differences in prefrontal alpha power asymmetry were observed under the described experimental conditions while comparing the states of calmness and emotional arousal of the participants for the three conditions of depression, anxiety, and stress.
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    Enhancing the Hybrid BCI Performance With the Common Frequency Pattern in Dual-Channel EEG
    (2019-07-01)
    Ko, Li-Wei
    ;
    Komarov, Oleksii
    ;
    Lin, Shih-Chuan
    ;
    交大名義發表
    ;
    生物資訊及系統生物研究所
    The brain-computer interface establishes a direct communication pathway between the human brain and an external device by recognizing specific patterns in cortical activities. The principle of hybridization stands for combining at least two different BCI modalities into a single interface with the aim of improving the information transfer rate by increasing the recognition accuracy and number of choices available for the user. This study proposes a simultaneous hybrid BCI system that recognizes the motor imagery (MI) and the steady-state visually evoked potentials (SSVEP) using the EEG signals from a dual-channel EEG setting with sensors placed over the central area (C3 and C4 channels). The data processing implements a supervised optimization algorithm for the feature extraction, named the common frequency pattern, which finds the optimal spectral filter that maximizes the separability of the data by classes. The experiment compares the classification accuracy in a two-class task using the MI, SSVEP and hybrid approaches on seventeen healthy 18-29 years old subjects with various dual-channel setups and complete set of thirty EEG electrodes. The designed system reaches a high accuracy of 97.4 +/- 1.1% in the hybrid task using the C3-C4 channel configuration, which is marginally lower than the 98.8 +/- 0.5% accuracy achieved with the complete set of channels while applying the support vector classifier; in the plain SSVEP task the accuracy drops from 91.3 +/- 3.9% to 86.0 +/- 2.5% while moving from the occipital to central area under the dual-channel condition. The results demonstrate that by combining the principles of hybridization and data-driven spectral filtering for the feature selection it is feasible to compensate a lack of spatial information and implement the proposed BCI using a portable few channel EEG device even under sub-optimal conditions for the sensors placement.