Browsing by Author "Lai, Kuan-Lin"

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Classification of Migraine Stages based on Resting-State EEG Power
(2015)
Cao, Ze-Hong
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Ko, Li-Wei
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Lai, Kuan-Lin
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Huang, Song-Bo
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Wang, Shuu-Jiun
Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will suffer migraine attacks, therefore, early detection becomes an important issue, especially for low-frequency migraine patients who have less than 5 times attacks per month. The main goal of this study is to develop a migraine-stage classification system based on migraineurs\' resting-state EEG power. We collect migraineurs\' 01 and 02 EEG activities during closing eyes from occipital lobe to identify pre-ictal and non-pre-ictal stages. Self-Constructing Neural Fuzzy Inference Network (SONFIN) is adopted as the classifier in the migraine stages classification which can reach the better classification accuracy (66%) in comparison with other classifiers. The proposed system is helpful for migraineurs to obtain better treatment at the right time.
Exploring resting-state EEG complexity before migraine attacks
(2018-06-01)
Cao, Zehong
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Lai, Kuan-Lin
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Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Chou, Chien-Chen
Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p<0.05) but not in the occipital area. The measurement of test-retest reliability (n=8) using the intra-class correlation coefficient was good with r1=0.73 (p=0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (764%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or normalization of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.
Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
(2020-01-01)
Cao, Zehong
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Lin, Chin-Teng
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Lai, Kuan-Lin
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Ko, Li-Wei
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King, Jung-Tai
Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 $ \pm $ 6 & x0025; and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.
Habituation of Steady-State Visual Evoked Potentials in response to High-Frequency Polychromatic Foveal Visual Stimulation
(2013)
Kuo, Heng-Yuan
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Chiu, George C.
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Zao, John K.
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Lai, Kuan-Lin
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Gruber, Allen
In an attempt to develop safe and robust methods for monitoring migraineurs\' brain states, we explores the feasibility of using white, red, green and blue LED lights flickering around their critical flicker fusion (CFF) frequencies as foveal visual stimuli for inducing steady-state visual evoked potentials (SSVEP) and causing discernible habituation trends. After comparing the habituation indices, the multi-scale entropies and the time dependent intrinsic correlations of their SSVEP signals, we reached a tentative conclusion that sharp red and white light pulses flickering barely above their CFF frequencies can replace commonly used 13Hz stimuli to effectively cause SSVEP habituation among normal subjects. Empirical results showed that consecutive short bursts of light can produce more consistent responses than a single prolonged stimulation. Since these high frequency stimuli do not run the risk of triggering migraine or seizure attacks, further tests of these stimuli on migraine patients are warranted in order to verify their effectiveness.
Resting-state EEG power and coherence vary between migraine phases
(2016-11-02)
Cao, Zehong
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Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Lai, Kuan-Lin
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Yang, Albert C.
Background: Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases. Methods: We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence of delta (1-3.5 Hz), theta (4-7.5 Hz), alpha (8-12.5 Hz), and beta (13-30 Hz) bands were calculated in the frontal, central, temporal, parietal, and occipital regions. Results: Fifty patients with episodic migraine (1-5 headache days/month) and 20 HCs completed the study. Patients were classified into inter-ictal, pre-ictal, ictal, and post-ictal phases (n = 22, 12, 8, 8, respectively), using 36-h criteria. Compared to HCs, inter-ictal and ictal patients, but not pre- or post-ictal patients, had lower EEG power and coherence, except for a higher effective connectivity in fronto-occipital network in inter-ictal patients (p<.05). Compared to data obtained from the inter-ictal group, EEG power and coherence were increased in the pre-ictal group, with the exception of a lower effective connectivity in fronto-occipital network (p<.05). Inter-ictal and ictal patients had decreased EEG power and coherence relative to HCs, which were "normalized" in the pre-ictal or post-ictal groups. Conclusion: Resting-state EEG power density and effective connectivity differ between migraine phases and provide an insight into the complex neurophysiology of migraine.
Steady-State Visual Evoked Potential based Classification System for Detecting Migraine Seizures
(2013)
Ko, Li-Wei
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Lai, Kuan-Lin
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Huang, Pei-Hua
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Lin, Chin-Teng
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Wang, Shuu-Jiun
The recurrent migraine attacks between interictal phenomenon is triggered by the migraineurs' brain lacking for habituation, due to the stimulations from the outside world that increase the excitability of brain activity, which have been considered as the possible reasons for migraine seizure. The variation of habituation level within the migraine cycle is proposed to be a critical symptom to describe the physiological states of migraine headache. This study proposed Steady-State Visual Evoked Potentials (SSVEP) examination to utilize habituation for classifying the different physiologic states of migraine cycle, and implement a classification system to determine different migraine states. The developed system may be extended to detect migraine seizure, and provide an opportunity to a clinically individual-based headache monitoring program, aiming for early migraine detection.