Comparative exploration on EEG signal filtering using window control methods

This paper studied the epilepsy signal in different frequency bands based on the average phase-synchronization time matrix (APSTM). APSTM algorithm can probe the change in the synchrony of the underlying system with high sensitivity, and be applied to multichannel, nonlinear time series in noisy. This paper uses the EEG filter provided by the EEGLAB to extract the four common waves : theta, alpha, beta and gamma, which are from the normal and epilepsy EEG signals. The four frequency bands of EEG signals are analysed based on the APSTM algorithm, and the results show that the APSTM determinant can distinguish the epilepsy from the normal EEG signals clearly. And when APSTM determinant detects epileptic discharge ,the theta wave is the most significant, and the beta wave can only generally detect epileptic discharge, while alpha and gamma waves are ineffective.

In this study cyclic changes of human sleep structure were examined. For whole-night polysomnograms of 35 healthy volunteers of both sexes, manual hypnograms were created and divided into NREM-REM cycles. EEG signals from C3-A2 derivation were analysed by computer using a Fast Fourier Transform (FFT). For consecutive NREM-REM cycles of individual sleep stages, EEG power density contents for delta, theta, alpha, sigma and beta waves were analysed. For consecutive sleep cycles, a clear decrease in NREM sleep duration, especially slow wave sleep duration, was obtained. In addition, a decrease in power density of delta waves was observed. For consecutive sleep cycles, increases in REM sleep duration and in power density of theta and alpha waves were obtained. In consecutive sleep cycles, high amplitude delta slow waves are replaced by higher frequency and lower amplitude waves. Thus stages of NREM sleep are replaced by stages of REM.

This study aims to analyze the relationship between brainwave activity and the learning readiness of students with disabilities by utilizing EEG (Electroencephalography) signals. EEG signals are used to detect the brain's electrical activity that reflects mental states, including focus, concentration, and readiness to receive learning. This study was conducted at SLB 'Aisyiyah Krian with a quantitative approach through EEG signal measurement before and during the learning process. The results showed a significant correlation between the dominance of alpha and beta waves with learning readiness, while the dominance of theta and delta waves indicated unpreparedness. These findings provide a foundation for the development of a more inclusive, objective, and adaptive neurotechnology-based learning approach for students with disabilities.

In this paper we propose some new methods for detecting emotions in EEG signals, using both time analysis and frequency analysis of signals. In the last section of the paper we present a method of classifying EEG signals using a feedforward neural network. EEG signals were acquired on a single channel, using a laboratory equipment produced by BIOPAC, and the subject was relaxed with his eyes open or in one of the states of joy, anger, and music listening for about 60 seconds. Separate analyzes were also performed for the 4 frequency bands of the EEG signals: alpha, beta, theta and delta waves.

Electroencephalographic (EEG) abnormalities are seen in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) with characteristic features of cognitive impairment. The most common findings of EEG features in AD and MCI patients are increased relative power of slow oscillations (delta and theta rhythms) and decreased relative power of fast oscillations (alpha, beta and gamma rhythms). However, impairments in cognitive processes in AD and MCI are not sufficiently reflected by brain oscillatory activity in a particular frequency band. MCI patients are at high risk of progressing to AD. Cross-frequency coupling (CFC), which refers to coupling between different frequency bands, is a crucial tool for comprehending changes in brain oscillations and cognitive performance. CFC features exhibit some specificity in patients with AD and MCI, but a comparison between CFC features in individuals with these disorders is still lacking. The aim of this study was to explore changes in CFC properties in MCI and AD and to explore the relationship between CFC properties and multiple types of cognitive functional performance. We recorded resting-state EEG (rsEEG) signals in 46 MCI patients, 43 AD patients, and 43 cognitively healthy controls (HCs) and analyzed the changes in CFC as well as the relationship between CFC and scores on clinical tests of cognitive function. Multiple couplings between low-frequency oscillations and high-frequency oscillations were found to be significantly enhanced in AD patients compared to those of HCs and MCI, while delta-gamma as well as theta-gamma couplings in the right temporal and parietal lobes were significantly enhanced in MCI patients compared to HCs. Moreover, theta-gamma coupling in the right temporal lobe tended to be stronger in MCI patients than in HCs, and it was stronger in AD than in MCI. Multiple CFC properties were found to correlate significantly with various cognitive domains, especially the memory function domain. Overall, these findings suggest that AD and MCI patients must use more neural resources to maintain a resting brain state and that alterations in theta-gamma coupling in the temporal lobe become progressively obvious during disease progression and are likely to be a valuable indicator of MCI and AD pathology.

ABSTRACT
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\nPutri, Anita Kurnia. 2020. “Analisis Kontrastif Adverbia Tinkatan Penanda Negasi dalam Bahasa Jepang dan Bahasa Jepang”. Thesis, department of Japanese Language and Culture, Faculty of Humanities, Diponegoro University. The Advisor: Lina Rosliana, S.S., M.Hum.
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\nThe problems that will be discussed in this thesis include: 1. What is the structure and meaning of the adverb in the level of negation markers in Japanese sentences? 2. What is the structure and meaning of the adverb at the level of the negation marker in Indonesian sentences? 3. How are the adverbs of the level of negation markers similar and different in Japanese and Indonesian sentences?
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\nMeanwhile, the purpose of this study is to describe the structure and meaning of adverbs in Japanese and Indonesian sentences. After knowing the structure and meaning, we will examine the similarities and differences in the structure and meaning of the adverbial levels of the negation markers of the two languages.
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\nIn analyzing the data, this study used the agih method followed by contrastive techniques. The data were obtained from the Japanese language website and news articles in Indonesian. Then the data is presented in words or descriptive.
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\nBased on the research, the structural similarities are both followed by verbs, adjectives. While the difference, adverbs in Japanese can be followed by phrases or clauses and negative forms, Indonesian adverbial followed by nouns. In meaning, the adverbs of the level of negation markers in Japanese and Indonesian indicates the meaning of the situation, the action is under adequate. And in terms of meaning it has 7 differences.
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\nKeywords : contrastive, adverbs, teido no fukushi
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BackgroundHighly structured electroencephalography (EEG) oscillations can occur in adults during etomidate-induced general anesthesia, but the link between these two phenomena is poorly understood. Therefore, in the present study, we investigated the electroencephalogram dynamics of etomidate-induced loss of consciousness (LOC) in order to understand the neurological mechanism of etomidate-induced LOC.MethodsThis study is a prospective observational study. Etomidate-induced anesthesia was performed on eligible patients undergoing elective surgery. We analyzed EEG data from 20 patients who received etomidate for the induction of general anesthesia. We used power spectra and coherence methods to process and analyze the EEG data. Our study was based on 4-channel EEG recordings.ResultsCompared with the baseline (awake period), etomidate induced an increase in power in delta, theta, alpha and beta waves during LOC. Compared with the awake period, the delta-wave (1–4 Hz), alpha-wave(8–13 Hz), and theta-wave(4–8 Hz) coherence increased significantly during LOC, while the slow-wave (< 1 Hz) coherence decreased. However, the delta wave (1.0–4.0 Hz) during etomidate-induced LOC was more coherent than during the awake period (1.86–3.17 Hz, two-group test for coherence, p < 0.001).ConclusionsThe neural circuit mechanism of etomidate-induced LOC is closely related to the induction of oscillation in delta, theta, alpha and beta waves and the enhancement of delta-wave coherence.Trial registrationChiCTR1800017110

Background. The method of anterior decompression (discectomy) for monosegmental stenosis of the cervical spinal canal is subjectively assessed as highly effective by both neurosurgeons and the vast majority of patients. However, the objective criteria for the early effectiveness of this intervention have not been sufficiently studied to date. Aim of the study to evaluate the dynamics of electroencephalography parameters in the preoperative and early postoperative period in patients with monosegmental stenosis of the cervical spine. Materials and methods. Electroencephalography studies were conducted on 22 patients of the neurosurgery department, before and after surgical treatment under code A16.23.085.001. The spectral composition of the bioelectrical activity of the brain (alpha, beta, delta and theta rhythms), as well as the coherence of oscillations in selected leads, were studied. Results. In the preoperative period, a disorganized type of electroencephalogramm was recorded in the examined group according to the classification of E.A. Zhirmunskaya; there was a decrease in the index and amplitude of the alpha rhythm in the parietal-occipital leads; predominance of high-amplitude beta activity in the frontal-central leads; presence of diffuse delta activity of the average index and theta activity of the average index, localized in leads P3, P4, C3, C4. On the 5th day after the operation, an organized type of electroencephalography was recorded on the encephalogram; a well-modulated alpha rhythm was observed, with a frequency in the range from 9 to 10 Hz, and an amplitude of 70 to 80 microvolts. Low-amplitude beta rhythm activity was recorded in the frontocentral leads. Low-amplitude delta and theta rhythms were recorded in the parieto-occipital leads and had a low index. Overall, the obtained data indicated positive dynamics in the restoration of cortical rhythms. Conclusion. The positive dynamics of cerebral cortex biorhythms in the early postoperative period allows the use of the electroencephalography method to objectify the results of surgical treatment, and in the future for the purposes of neurorehabilitation.

A prospective case control investigation was conducted to assess corticomuscular coherence (CMC) under different balance conditions in order to identify alterations that may arise due to forward head posture (FHP). Sixty-four participants (between the ages of 18–25) were recruited for this study. Participants were free from any musculoskeletal symptoms and matched for relevant demographic variables. Participants were assessed for FHP using the craniovertebral angle (CVA) and separated into either the normal head posture (NHP) group by CVA > 50° or the FHP group by CVA < 50°. Participants were evaluated using the Biodex balance system while both EEG and EMG signals which were acquired simultaneously to investigate CMC. Balance was investigated under four conditions increasing in challenge: (1) standing with eyes open, (2) standing with eyes closed, (3) unstable standing with eyes open with the Biodex set on level 8, and (4) unstable standing with eyes closed with the Biodex at level 8. In addition to recording delta, beta, theta, alpha, and gamma waves, we calculated both peak and average coherence values from the CMC analysis. Statistically significant differences were identified for the FHP group vs. the NHP group for both peak (p < .001) and average CMC values (p < .001) and for the interaction of balance difficulty (stage 1 vs. stage 4), p < .01. Similarly, all brain waves (delta, beta, theta, alpha, and gamma) demonstrated statistically significant differences between the NHP and FHP groups (p < .001) and for increasing balance difficulty across the four stages, p < .05. This study demonstrates that FHP is associated with a significant elevation of CMC, a unique compensatory burden on the brain, particularly during physically challenging balance tasks.

Relevance of electroencephalogram assessment in amyloid and tau pathology in rat

A better understanding of the network responses of cortical activities during rest and cognitive tasks is necessary. Therefore, in this study, we aimed to evaluate cerebral activities during attentional tasks by using mobile electroencephalography, identifying the types of attentional components and brain waves. In this experimental study, we enrolled 12 healthy young adults. The attentional tasks comprised parts A and B of the Trail-Making Test (TMT). Nineteen electroencephalography electrodes were placed over various brain regions. The Wilcoxon signed-rank test was used to examine the differences in power levels between the rest and TMT conditions. During TMT part A, the electroencephalography power level of the delta waves was significantly higher in the right frontal, left central, left occipital, left inferior frontal, right mid-temporal, right posterior temporal, and middle parietal areas (P < .05) than those during the resting state; that of the alpha waves was significantly lower in the left posterior temporal area (P = .006); and that of the high gamma waves was significantly lower in the left parietal (P = .05) and left occipital (P = .002) areas. During TMT part B, the electroencephalography power level of the beta waves was significantly higher in the right frontal area (P = .041) than that during the resting state, and that of the low gamma waves was significantly higher in the left frontal pole, right frontal, and right inferior frontal areas (P < .05). During the focused attentional task, the power level of the delta waves increased and that of the alpha waves decreased, and during the alternating attentional task, those of both the beta and gamma waves increased. The delta waves were related to the whole brain, the alpha and high gamma waves to the left posterior lobe, and the beta and low gamma waves to both frontal lobes. These findings contribute to the basic knowledge necessary to develop new attentional assessment methods for clinical situations.

Breastfeeding is a process of nursing a baby with milk straight from the women’s breast. It is suggested that a baby should be breastfed within one hour of birth, exclusively breastfed for the first six months, and then breastfed until age two with age-appropriate, nutritionally sufficient and harmless complementary diets. This paper focuses on the examination of breastfeeding women on their stress level and the brainwave characteristics. The brainwave signals were documented using wireless Electroencephalogram (EEG) equipment via Bluetooth while. The stress level was performed using a questionnaire. The EEG signals were derived using an existing system with the electrodes attached to human scalp. These electrodes measure the electrical signals which are produced by the activities of the neurons in the human brain, such as Delta, Theta, Alpha and Beta waves in the brain to determine the Brainwave Balancing Index (BBI) and also the brain hemispheric dominance which is right or left dominance. The outcome presented balanced BBI of the breastfeeding women after the breastfeeding session, although most of them were high in stress. Right brain dominance was also recorded for the majority of them for both sessions, before and after breastfeeding. Statistical investigation leads to no significant correlations between stress and BBI of breastfeeding women after breastfeeding session, in terms of breastfeeding method and in terms of breastfeeding women’ categories.

Drowsiness presents major safety concerns for tasks that require long periods of focus and alertness. While there is a body of work on drowsiness detection using EEG signals in neuroscience and engineering, there exist unanswered questions pertaining to the best mechanisms to use for detecting drowsiness. Targeting a range of practical safety-awareness applications, this study adopts a machine learning based approach to build support vector machine (SVM) classifiers to distinguish between awake and drowsy states. While broadband alpha, beta, delta, and theta waves are often used as features in the existing work, lack of widely agreed precise definitions of such broadband signals and difficulty in accounting for interpersonal variability has led to poor classification performance as demonstrated in this study. Furthermore, the transition from wakefulness to drowsiness and deeper sleep stages is a complex multifaceted process. The richness of this process calls for inclusion of sub-band features for more accurate drowsiness detection. To shed light on the effectiveness of sub-banding, we quantitatively compare the performances of a large set of SVM classifiers trained upon a varying number of 1Hz sub band features. More importantly, we identify a compact set of neuroscientifcally motivated EEG features and demonstrate that the resulting classifier not only outperforms traditional broadband based classifiers but also is on a par with or superior than the best sub-band classifiers found by thorough search in a large space of 1Hz sub band features.

The Brain is the main controller of the human body consisting of neurons which generate electrical signals to control the body. Different neurological diseases can cause brain abnormalities such as ALS that affect the electrical activity of the brain which can be seen in the EEG signal. Therefore, these abnormalities can be detected by analyzing the EEG signal. As the Alpha and Beta waves are recorded during the active state of the brain, the effect of abnormalities can be seen in the waves. This study proposes a technique to detect the brain abnormalities and detect ALS syndromes based on Alpha and Beta waves. In this study, the Alpha and Beta waves are extracted from the raw EEG signal by discrete wavelet transformation. Here, the raw EEG signal is divided into several orthogonal wavelets using Doubechis 4 wavelet transform; Alpha and Beta wave frequency components are selected and reconstructed. Then these waves can be compared between different types of ALS patients and normal people based on amplitude and event related potential. The result showed great variation in amplitude as well as event related potential for different ALS patients and normal people. The proposed technique could be used in detecting abnormalities and their severity. It could also help in detecting ALS at a primary stage.

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method