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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
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Ko, Li-Wei
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Jung, Tzyy-Ping
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交大名義發表
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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.
Discrimination of Severity of Alzheimer's Disease with Multiscale Entropy Analysis of EEG Dynamics
(2020-02-01)
Hsu, Chang Francis
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Chao, Hsuan-Hao
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Yang, Albert C.
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Yeh, Chih-Wei
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Hsu, Long
Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer's disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer's disease.
Temporal EEG Imaging for Drowsy Driving Prediction
(2019-12-01)
Cheng, Eric Juwei
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Young, Ku-Young
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Lin, Chin-Teng
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver's EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.
Development and Validation of an EEG-Based Real-Time Emotion Recognition System Using Edge AI Computing Platform With Convolutional Neural Network System-on-Chip Design
(2019-12-01)
Fang, Wai-Chi
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Wang, Kai-Yen
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Fahier, Nicolas
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Ho, Yun-Lung
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Huang, Yu-De
This study proposed an electroencephalogram (EEG)-based real-time emotion recognition hardware system architecture based on multiphase convolutional neural network (CNN) algorithm implemented on a 28-nm technology chip and on field programmable gate array (FPGA) for binary and quaternary classification. Sample entropy, differential asymmetry, short-time Fourier transform, and a channel reconstruction method were used for emotion feature extraction. In this work, six EEG channels were selected (FP1, FP2, F3, F4, F7, and F8), and EEG images were generated from spectrogram fusions. The complete CNN architecture included training and acceleration for efficient artificial intelligence (AI) edge application, and we proposed a multiphase CNN execution method to accommodate hardware resource constraints. Datasets of 32 subjects from the DEAP database were used to validate the proposed design, exhibiting mean accuracies for valance binary classification and valance-arousal quaternary classification of 83.36% and 76.67%, respectively. The core area and total power consumption of the CNN chip were 1.83 x 1.83 mm(2), respectively, and 76.61 mW. The chip operation was validated using ADVANTEST V93000 PS1600, and the training process and real-time classification processing time took 0.12495 ms and 0.02634 ms for each EEG image, respectively. The proposed EEG-based realtime emotion recognition system included a dry electrode EEG headset, feature extraction processor, CNN chip platform, and graphical user interface, and the execution time costed 450 ms for each emotional state recognition.
Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study
(2019-09-01)
Chikara, Rupesh Kumar
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Ko, Li-Wei
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交大名義發表
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生物科技學系
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生物資訊及系統生物研究所
Auditory alarms are used to direct people's attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.
Infant Brain Detects Inconsistency Between Belief and Action: An Electroencephalography Study
(2019-09-01)
Chou, Yu-Ju
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King, Jung-Tai
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腦科學研究中心
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Brain Research Center
Neural Comodulation of Independent Brain Processes Related to Multitasking
(2019-06-01)
Tien-Thong Nguyen Do
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Chuang, Chun-Hsiang
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Hsiao, Shih-Jie
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Lin, Chin-Teng
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Wang, Yu-Kai
Distracted driving is regarded as an integrated task requiring different regions of the brain to receive sensory data, coordinate information, make decisions, and synchronize movements. In this paper, we applied an independent modulator analysis (IMA) method to temporally independent electroencephalography (EEG) components to understand how the human executive control system coordinates different brain regions to simultaneously perform multiple tasks with distractions presented in different modalities. The behavioral results showed that the reaction time (RT) in response to traffic events increased while multitasking. Moreover, the RT was longer when the distractor was presented in an auditory form versus a visual form. The IMA results showed that there were performance-related IMs coordinating different brain regions during distracted driving. The component spectral fluctuations affected by the modulators were distinct between the single- and dual-task conditions. Specifically, more modulatory weight was projected to the occipital region to address the additional distracting stimulus in both visual and auditory modality in the dual-task conditions. A comparison of modulatory weights between auditory and visual distractors showed that more modulatory weight was projected to the frontal region during the processing of the auditory distractor. This paper provides valuable insights into the temporal dynamics of attentional modulation during multitasking as well as an understanding of the underlying brain mechanisms that mediate the synchronization across brain regions and govern the allocation of attention in distracted driving.
Enhancing the Hybrid BCI Performance With the Common Frequency Pattern in Dual-Channel EEG
(2019-07-01)
Ko, Li-Wei
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Komarov, Oleksii
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Lin, Shih-Chuan
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交大名義發表
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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.
Beamformer Source Estimation Improves Accuracy of Motor-Imagery-based Brain Computer Interface
(2018-01-01)
Chan, Hui-Ling
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Wang, Jung-Wei
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Chen, Yong-Sheng
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交大名義發表
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分子醫學與生物工程研究所
Motor-imagery-based brain computer interface has advantages in high information transition rate and has potential to achieve high portability without using a display for visual stimulation. However, electroencephalographic data is a mixture of neural activity from various brain locations and also external artifacts. This paper presents a novel method utilizing source level features computed based on maximum contrast beamformer. The estimated source activity has the maximum contrast between the power during the reference period and the period that event-related synchronization or desynchronization emerges. Moreover, this method utilizes the classification structure based on divide-and-conquer concept to identify four classes of motor imageries, including left hand, right hand, tongue, and foot. The experimental results demonstrate the improvement in classification accuracy by using the proposed method.
Monetary Reward and Punishment to Response Inhibition Modulate Activation and Synchronization Within the Inhibitory Brain Network
(2018-03-01)
Chikara, Rupesh K.
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Chang, Erik C.
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Lu, Yi-Chen
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Lin, Dar-Shong
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Lin, Chin-Teng
A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks.

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🏛️國立陽明交通大學機構典藏

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