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Journal articles on the topic 'EEG wave'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Lim, Miranda, Christina Reynolds, Carolyn Jones, Sophia Lambert, Nadir Balba, Jonathan Elliott, and Yo-El Ju. "799 Automated Detection of Slow Wave Coherence in Sleep EEG: A potential neurophysiological correlate of cognitive decline." Sleep 44, Supplement_2 (May 1, 2021): A311. http://dx.doi.org/10.1093/sleep/zsab072.796.

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Abstract Introduction A bidirectional relationship exists between sleep disruption and neuropathology in Alzheimer’s disease (AD). The sleep electroencephalogram (EEG) is a highly stereotyped, direct neurophysiological window into brain function; prior studies have identified abnormalities in EEG slow waves in early AD. EEG coherence across channels during sleep, a normally highly coherent brain state, could be an indicator of network coordination across brain regions. Accordingly, altered slow wave coherence during sleep may be an early indicator of cognitive decline. Methods EEG was collected during an attended overnight polysomnogram (PSG) from a community-based cohort of older subjects (n=44, average age = 71), approximately 25% of whom met criteria for mild cognitive impairment or early AD. Files were exported to EDF and a slow wave peak detector was implemented in MATLAB to count the number of slow wave oscillations, with automated artifact rejection, across 6 EEG leads standard for PSG (C3, C4, F3, F4, O1, and O2). Slow wave coherence was inferred when slow waves occurred in temporal synchrony across channels within 100 ms. Results Subjects with cognitive impairment showed significantly reduced total sleep time and time spent in rapid eye movement (REM) sleep compared to age-matched controls. EEG slow wave coherence was reliably quantified during wake, non-REM stages N1, N2, N3, and REM vigilance states as well as during transition periods between sleep stages. Using this algorithm, specific signatures of slow wave propagation during sleep were identified, including increased variability in slow wave activity and coherence, that appeared more prominent in subjects with impaired cognition. Conclusion EEG slow wave coherence during sleep and wake states can be calculated by applying automated algorithms to PSG data, and may be associated with cognitive impairment. Support (if any) NIH R01 AG059507

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Camfield, Peter, Kevin Gordon, Carol Camfield, John Tibbies, Joseph Dooley, and Bruce Smith. "EEG Results are Rarely the Same if Repeated within Six Months in Childhood Epilepsy." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 22, no. 4 (November 1995): 297–300. http://dx.doi.org/10.1017/s0317167100039512.

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AbstractObjectiveTo assess the reliability of interictal spike discharge in routine electroencephalography (EEG) testing in children.MethodEEG results of all children diagnosed in Nova Scotia with epilepsy onset between 1977–85 (excluding myoclonic, akinetic-atonic and absence) were reviewed. The results of the EEG at time of diagnosis (EEG1) were compared with those of a second EEG (EEG2) within 6 months.ResultsOf 504 children with epilepsy, 159 had both EEG1 and EEG2. EEG2 was more likely ordered if EEG1 was normal or showed focal slowing but less likely if EEG1 contained sleep (p < 0.05). EEG1 and EEG2 were both normal in 23%. If EEG1 was abnormal, there was a 40–70% discordance for the type of abnormality on EEG2. Abnormalities were present on both EEG1 and EEG2 in 67 cases. Of the 42/67 with major focal abnormalities on EEG1, 7 had only generalized spike wave on EEG2. Of the 17/67 with only generalized spike wave on EEG 1, 7 showed only major focal abnormalities on EEG2. Statistical testing showed low Kappa scores indicating low reliability.ConclusionsThe interictal EEG in childhood epilepsy appears to be an unstable test. A repeat EEG within 6 months of a first EEG may yield different and sometimes conflicting information.

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Bhuyan, Rimpy, Wasima Jahan, and Narayan Upadhyaya. "Interictal wave pattern study in EEG of epilepsy patients." International Journal of Research in Medical Sciences 5, no. 8 (July 26, 2017): 3378. http://dx.doi.org/10.18203/2320-6012.ijrms20173526.

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Background: EEG or Electroencephalogram is the most important diagnostic tool to detect Epilepsy. Interictal period is the time interval between two seizure episodes of an Epileptic patient. Certain wave patterns appear in the interictal period in the EEG which might predict the onset of a seizure or may give information about the last seizure attack. The aim of the study was to know how the interictal wave patterns help in diagnosing and classifying Epilepsy casesMethods: The present study was done in the Department of Physiology in association with the Department of Neurology, Assam Medical College and Hospital, Dibrugarh, Assam from June 2014 to May 2015. 113 clinically diagnosed cases of Epilepsy were studied and analyzed through Electro-encephalogram using the internationally accepted 10-20 electrode placement method. The interictal period was enquired in the history and the wave patterns that appeared in the EEG were recorded. The EEG findings were compared with the clinical diagnosis.Results: The IEDs detected were mainly of four types: Sharp waves, Spikes, Spike and wave and Polyspikes. It was found that the sharp waves (88.89%) were the predominant waveforms in the IEDs detected and this was followed by the ‘3 Hz spike and wave pattern’. It was also seen that the ‘3 Hz spike and wave pattern’ was associated with ‘Absence seizures’. And Myoclonic seizures were associated with polyspikes.Conclusions: It is hereby concluded that certain wave patterns in EEG appear in certain types of epilepsy that can be clinically correlated for proper diagnosis of epilepsy.

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Min, Wanli, and Gang Luo. "Medical Applications of EEG Wave Classification." CHANCE 22, no. 4 (September 2009): 14–20. http://dx.doi.org/10.1080/09332480.2009.10722978.

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Min, Wanli, and Gang Luo. "Medical applications of EEG wave classification." CHANCE 22, no. 4 (December 2009): 14–20. http://dx.doi.org/10.1007/s00144-009-0037-7.

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Carlson, Chad. "EEG Wave of the Future: The Video-EEG and fMRI Suite?" Epilepsy Currents 13, no. 5 (September 2013): 205–6. http://dx.doi.org/10.5698/1535-7597-13.5.205.

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7

Jaseja, Harinder, and Bhanu Jaseja. "EEG spike versus EEG sharp wave: Differential clinical significance in epilepsy." Epilepsy & Behavior 25, no. 1 (September 2012): 137. http://dx.doi.org/10.1016/j.yebeh.2012.05.023.

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Kuzmich, G. V., M. Yu Bobylova, K. Yu Mukhin, O. A. Pylaeva, L. Yu Glukhova, A. S. Bagdasaryan, and A. Yu Zakharova. "EEG findings in patients with angelman syndrome. Notched slow waves and age-specific characteristics of the main EEG patterns." Russian Journal of Child Neurology 16, no. 1-2 (July 30, 2021): 42–57. http://dx.doi.org/10.17650/2073-8803-2021-16-1-2-42-57.

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Angelman syndrome (AS) is a genetic disorder caused by a mutation in the maternal copy of the UBE3A gene and characterized by typical clinical manifestations (such as mental retardation, difficulty walking, and laughter) and specific changes on the electroencephalogram (EEG).The aim of this study was to analyze age-specific characteristics of the main EEG patterns, including high-amplitude frontal delta activity with spikes, slow-wave delta-theta activity with spikes in the posterior regions, and diffuse continuous rhythmic theta activity. In addition to that, we assessed the frequency of a rare and highly specific for AS EEG pattern: notched slow waves.We have identified and described additional criteria for EEG during sleep: high index of pathological slow-wave activity and the ratio of pathological slow-wave activity index to epileptiform activity index during sleep. We also analyzed all EEG patterns at the age most significant for the detection of this syndrome (up to 3 years) and their age-specific dynamics.We covered the frequency and characteristics of EEG patterns rare in AS patients, such as three-phase bifrontal delta waves, reactive pathological activity in the posterior areas, EEG patterns of focal seizures originating from the posterior areas, benign epileptiform discharges of childhood, and migrating continuous slow-wave activity.We analyzed the differences between main EEG patterns in AS and frontal and occipital intermittent rhythmic delta activity (fIRDA and OIRDA patterns).

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Nissen, Christoph, Bernd Feige, Eric A. Nofzinger, Ulrich Voderholzer, Mathias Berger, and Dieter Riemann. "EEG Slow Wave Activity Regulation in Major Depression. EEG-Slow-Wave-Aktivitat bei Patienten mit Major Depression." Somnologie 10, no. 2 (May 2006): 36–42. http://dx.doi.org/10.1111/j.1439-054x.2006.00083.x.

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Ana-Maria, Ionescu, Dumitrescu Cătălin, Copaci Carmen, Iliescu Dan, Hangan Tony, and Bobe Alexandru. "Analysis and Detection of EEG Transient Waves During Sleep." ARS Medica Tomitana 24, no. 3 (November 1, 2018): 133–43. http://dx.doi.org/10.2478/arsm-2018-0025.

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Abstract Electroencephalogram (EEG) analysis consists of locating signal structtures in time and frequency. A detection method based on the Matching Pursuit Algorithms finds the suboptimal solution of the function optimal linear expansion over a redundant waveform dictionary. This paper has put forth a method for the automatic detection and analysis of transient waves during sleep based on the matching pursuit method with a real dictionary og Gabor functions. Each wave peak is described in terms of natural parameters. In this context, there have been confirmed several literature hypotheses regarding the spatial, temporal, and frequency distribution of transient waves during sleep, and their relationships with slow wave brain activity. Mastery and expertise in clinical EEG interpretation is one of the most desirable disgnostic clinical skills in interpreting seizures, epilepsy, sleep disorder, biomarkers for early disgnosis of Parkinson’s and Alzheimer’s disease, and other neurocognitive studies.

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Millichap, J. Gordon. "Clinical Significance of Generalized Spike/Wave EEG." Pediatric Neurology Briefs 14, no. 2 (February 1, 2000): 11. http://dx.doi.org/10.15844/pedneurbriefs-14-2-3.

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Millichap, J. Gordon. "Spike and Wave EEG Abnormalities in ADHD." Pediatric Neurology Briefs 27, no. 9 (September 1, 2013): 72. http://dx.doi.org/10.15844/pedneurbriefs-27-9-10.

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Kim, Jung-Sook, and Jang-Young Chung. "An EEG Encryption Scheme for Authentication System based on Brain Wave." Journal of Korea Multimedia Society 18, no. 3 (March 30, 2015): 330–38. http://dx.doi.org/10.9717/kmms.2015.18.3.330.

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Chenghu, Cui, Santichai Wicha, and Roungsan Chaisricharoen. "Analysing the EEG Signal Effectiveness of Chiang Rai Arabica Drip Coffee on Individual Human Brainwave." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 13, no. 2 (March 14, 2020): 178–87. http://dx.doi.org/10.37936/ecti-cit.2019132.194581.

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This study focused on the impact of local Arabica coffee on the level of attention of individual brain waves, and how coffee affects Human EEG Frequency. Local Arabica coffee is adopted in this study as a medium to wake up the Beta wave. The Personal brainwave data is then recorded through EEG equipment and classified. The result showed that local coffee is helping to improve people's attention level — the study conducted on fifty participants: twenty-five males and twenty-five females aged between twenty to thirty years old. Brainwaves or Electroencephalography are collected twice before and after drinking coffee to compare the effects of Arabica on human brain waves by using NeuroSky mindwave mobile. The paired sample t-test test was employed for comparing two groups of Beta brainwaves experiment. Besides, the k-means algorithm is used to perform data mining on brain waves, and the differential brain wave signal data is clustered and divided into three levels. The experimental results showed that there was a statistically significant difference between the two paired samples. Therefore, the results confirmed that local Arabica coffee has a direct impact on personal attention.

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Tsuji, Yoichi, Takefumi Usui, Yasuhisa Sato, and Kazuyuki Nagasawa. "Development of Automatic Scoring System for Sleep EEG Using Fuzzy Logic." Journal of Robotics and Mechatronics 5, no. 3 (June 20, 1993): 204–8. http://dx.doi.org/10.20965/jrm.1993.p0204.

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Determination of the EEG sleep stages by clinicians is a time consuming task, because of a great amount of record obtained during one night. Therefore, an effective computer technique for an automatic processing EEG is strongly desired. We tried to make an automatic scoring system for human sleep EEG stages by a computer. The specific EEG wave form (e.g. delta, theta, alpha or spindle wave) was detected by means of a specific algorithm using a Fuzzy logic and an ""if...then..."" procedure. The scoring results by this system and clinicians agreed with each epoch more than 72%. This system is an available system for the clinical sleep EEG.

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Balba, Nadir, Christina Reynolds, Mo Modarres, Alisha McBride, Selda Yildiz, Mary Heinricher, and Miranda Lim. "069 Chronic pain in Veterans with TBI is associated with decreased EEG slow wave coherence during NREM sleep." Sleep 44, Supplement_2 (May 1, 2021): A28—A29. http://dx.doi.org/10.1093/sleep/zsab072.068.

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Abstract Introduction Chronic pain and sleep disturbances are intricately linked to one another, especially in individuals with a history of traumatic brain injury (TBI) who are at greater risk for both symptoms. Although prior studies have analyzed differences in sleep electroencephalogram (EEG) in these clinical populations, the association between sleep EEG slow wave coherence and pain complaints is not fully examined or known. Our novel slow wave coherence approach may provide new insights into the relationship between TBI, chronic pain, and sleep Methods Ninety-six veterans were recruited and enrolled under a VA IRB-approved protocol. Participants completed a semi-structured clinical interview to determine their history of TBI, Symptom Impact Questionnaire Revised (SIQR), a measure of chronic pain complaints, and underwent an attended overnight in-lab polysomnogram (PSG). We developed a novel computational signal processing algorithm to identify and quantify EEG slow waves within 100 ms bins across the 6 standard PSG EEG channels. When a slow wave was simultaneously observed in 4 or more of the 6 leads, slow wave coherence was inferred, and a percentage of slow wave coherence across each of the sleep stages was then calculated for each subject. Results In our sample, 65 participants (67.7%) endorsed experiencing chronic pain lasting 3 months or longer, and 54 had a history of TBI (56.3%). Participants endorsing chronic pain had a significantly lowered percent of EEG slow wave coherence during NREM sleep than subjects without chronic pain (p = 0.01). NREM EEG slow wave coherence did not correlate with SIQR scores in subjects without TBI (r = -0.03, p = 0.90), but was significantly negatively correlated in subjects with TBI (r = -0.32, p = 0.02). Conclusion EEG slow wave coherence during NREM sleep is correlated with chronic pain complaints in Veterans with a history of TBI, and could be indicative of neuronal dysfunction during sleep. Further research on slow wave coherence is warranted to understand the underlying mechanisms for the association between chronic pain and poor sleep following TBI. Support (if any) D01 W81XWH-17-1-0423

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Wang, W. W., J. C. Li, and X. Wu. "Quantitative EEG Effects of Topiramate." Clinical Electroencephalography 34, no. 2 (April 2003): 87–92. http://dx.doi.org/10.1177/155005940303400208.

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Objective: The study is to invesigate the effect of topiramate (TPM) on EEG by means of quantitative pharmacoelectroencephalography (QPEEG). Methods: One dose of TPM was administrated to epileptics and healthy adults. The EEG samples were obtained prior to and at regular intervals within the 24 hours following the administration of TPM. The EEG activity was processed with power spectral analysis. Results: The power of slow wave, alpha 1 bands and total power increased after the administration of TPM, the power or slow wave in both occipital areas, and the total power of all scalp areas also increased. The percent of power increased at the theta band and alpha 1 band (healthy adults) or delta band, theta band (patients). Conclusion: TPM can change the EEG background activity. These changes are different from other antiepileptic drugs.

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Silva, Délrio F., Márcia Marques Lima, Luzinete V. A. t. Gonzalez, Odyna J. Lr Lopez, Renato Anghinah, Edmar Zanoteli, and José Geraldo C. lima. "Epilepsy with continuous spike-waves during slow wave sleep: a clinical and electroencephalographic study." Arquivos de Neuro-Psiquiatria 53, no. 2 (June 1995): 252–57. http://dx.doi.org/10.1590/s0004-282x1995000200011.

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We report four children with epilepsy with "continuous spike-waves during slow wave sleep" (CSWSS). The main clinical features were partial motor seizures, mental retardation and motor deficit. The EEG findings were characterized by nearly continuous (>85%) diffuse slow spike and wave activity in two patients, and localized to one hemisphere in two other cases during non-REM sleep. The treatment was effective in improving the clinical seizures, but not the EEG pattern. We believe that this epileptic syndrome has been overlooked and routine sleep EEG studies on epileptic children may disclose more cases of CSWSS.

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Sungwon Choi, Chang-Yil Ahn, and 제갈은주. "Depression and Baseline Prefrontal EEG Alpha Wave Asymmetry." Korean Journal of Clinical Psychology 27, no. 4 (November 2008): 1053–69. http://dx.doi.org/10.15842/kjcp.2008.27.4.016.

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Jang, Seok-Woo, In-gil Park, Dae-Kyeum Kim, and Hyun Choi. "The effect of hypersonic wave sound for EEG." Korean Society for Emotion and Sensibility 17, no. 2 (June 30, 2014): 101–10. http://dx.doi.org/10.14695/kjsos.2014.17.2.101.

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Scheib, C. "SEE 27 PHYSIOLOGICAL MECHANISM OF EEG WAVE GENERATION." Anesthesiology 87, Supplement (September 1997): 27SEE. http://dx.doi.org/10.1097/00000542-199709001-01142.

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Nissen, Christoph, Bernd Feige, Eric A. Nofzinger, Ulrich Voderholzer, Mathias Berger, and Dieter Riemann. "EEG slow wave activity regulation in major depression." Somnologie - Schlafforschung und Schlafmedizin 10, no. 2 (May 2006): 36–42. http://dx.doi.org/10.1007/j.1439-054x.2006.00083.x.

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Ogunjimi, L., A. Alabi, B. Osalusi, A. Muritala, A. Aderinola, and A. Ogunniyi. "Electroencephalographic Correlates of Cognition among Nigerian Women with Epilepsy on Anti-epileptic Monotherapy." Annals of Health Research 7, no. 2 (May 28, 2021): 165–78. http://dx.doi.org/10.30442/ahr.0701-08-127.

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Background: The prospect of EEG as a potential biomarker for detecting a cognitive decline in those living with epilepsy has not been extensively studied. Objective: To determine the relationship between electroencephalographic (EEG) changes and cognitive functions in Women with Epilepsy (WWE). Methods: The study involved 100 adult WWE aged between 16 and 40 years on Levetiracetam (LEV) or Carbamazepine (CAB) monotherapy. Zung Self-Reporting Depression Scale (ZSRDS) was used to assess the mood of participants while the Community Screening Interview for Dementia (CSID) was used to assess various cognition domains. Results: The frequency of Periodic Epileptiform Discharges (PED) (p = 0.008), delta waves and theta waves (p = 0.004) were higher in WWE with Cognitive Impairment (CI) compared to those without CI. Lower cognitive scores were seen among those with delta wave across the domains of cognition with statistical significance for language fluency (p = 0.039), language comprehension (p = 0.000), and total CSID (p = 0.000). WWE with PED had a lower mean total CSID score compared to those without PED (p = 0.019). The absence of alpha wave (p = 0.027), presence of delta wave (p = 0.013), slow frequency (p = 0.015) and PED (p = 0.031) were EEG predictors of cognitive impairment. Medication type (p = 0.016) and depression (p = 0.001) were the clinical predictors of cognitive impairment in WWE. Conclusion: The frequencies of PED and slow waves were higher in WWE with CI while the absence of alpha wave, presence of delta wave and PED were EEG predictors of CI.

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Peng, Jun Qiang, and Ping Dong Wu. "EEG Characters Research to Different Personality People." Applied Mechanics and Materials 138-139 (November 2011): 967–73. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.967.

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Purpose: To find the relationships between different personality people and their EEG. Method and Procedures: Using the psychology test method of Uchida-Kraepelin psycho-diagnostic test (UK Test) to sort the experimenters into different groups according to three aspects: startup capacity, excitement capacity and changeable capacity. At the same time, the experimenters’ EEG was measured. By analyzing the frequency and energy of EEG, the EEG characters of different personality people were found. Results and Conclusions: (1) EEG character of startup capacity is the amount of β-wave energy in the first several minutes of the experiment. (2) EEG character of excitement capacity is the amount and developing trend of α-wave energy in the experiment. (3) EEG character of changeable capacity is the fluctuation of β-wave energy. The results of the research can be used to increase the precision and adaptability of the BCI system.

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Lim, Jung Eun, Bo Hyeok Seo, Sun Hyun Kim, and Soon Yong Chun. "Study on EEG Feature Extraction under LED Color Exposure to Enhance the Concentration." Advanced Engineering Forum 2-3 (December 2011): 261–65. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.261.

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Since brain waves are expressed in a variety of frequency domains, they were used to analyze a correlation between colors and concentration. In this study, the brain wave reacting when exposed to colors was defined as a color brain wave (CBW). Also the colors on the table were changed during task performance to see colors’ influence on improving concentration and then the brave waves were measured for analysis on and comparison with the findings from the task performance. Based on the biometric data experiment conducted, it was confirmed that the findings during the task performance and those from EEG signals have a correlation and that human’s concentration is thus affected by changes of colors.

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Kim, Min Soo, Hak Dong Kim, Hee Don Seo, Kazuaki Sawada, and Makoto Ishida. "The Effect of Acupuncture at PC-6 on the Electroencephalogram and Electrocardiogram." American Journal of Chinese Medicine 36, no. 03 (January 2008): 481–91. http://dx.doi.org/10.1142/s0192415x08005928.

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The present study aims to examine the effect of acupuncture stimulation of an acupuncture point (PC-6) and nonacupuncture point on electroencephalograms (EEGs) and electrocardiograms (ECGs). We used EEG in 10 healthy subjects to investigate cortical activation during stimulation of acupuncture points (neiguan: PC-6) and nonacupuncture points. Our most interesting finding was the marked differences of amplitude of EEG power between acupuncture points and nonacupuncture points stimulation. Wavelet transform was used as the EEG signal processing method, because it has advantages in a time domain and frequency domain characteristics analysis. EEGs were collected from 16 channels, and the α-wave (8–13 Hz), β-wave (13–30 Hz), θ-wave (4–8 Hz) and δ-wave (0.5–4 Hz) were used as standards for frequency bands. According to the experiment results, EEG signals increased considerably after acupuncture stimulation; in each frequency band, the average amplitude was higher after acupuncture stimulation; ECG heart rates were faster by at least 10% after acupuncture stimulation. Consequently, it will be possible to verify the function of acupuncture stimulation on neiguan (acupuncture points; PC-6) more effectively.

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Kit, G. V. "ANALYSIS OF PEAK-WAVE DISCHARGES OF EEG WITH THE USE OF WAVELET TRANSFORMATIONS." Visnyk Universytetu “Ukraina”, no. 1 (28) 2020 (2020): 224–34. http://dx.doi.org/10.36994/2707-4110-2020-1-28-19.

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The method of analysis of electroencephalograms (EEG) on the basis of wavelet transformations is offered. Electroencephalogram (EEG) analysis is widely used in clinical practice for diagnosing such neurological diseases as epilepsy, Parkinson's disease and others. Traditional approaches to EEG analysis, generally accepted in the clinical diagnosis of diseases, are due to the fact that for a certain time after the stimulus, the EEG amplitudes are calculated at time intervals that depend on the frequency of signal quantization. Therefore, it is important to develop algorithms for classifying EEG signals using wavelet transforms. The analysis of peak-wave EEG discharges, which are indicators of the presence or absence of absence epilepsy, was performed. The EEG recording areas were decomposed into the main EEG frequency bands. Wavelet transform in combination with artificial neural networks makes it possible to implement a classifier based on the energy distribution of the components of the EEG signal. Determining the activity of individual components of EEG signals, as well as the materiality of the processes that take place in the sources of these waves, may be the subject of further research.

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Gronfier, Claude, Florian Chapotot, Laurence Weibel, Christophe Jouny, François Piquard, and Gabrielle Brandenberger. "Pulsatile cortisol secretion and EEG delta waves are controlled by two independent but synchronized generators." American Journal of Physiology-Endocrinology and Metabolism 275, no. 1 (July 1, 1998): E94—E100. http://dx.doi.org/10.1152/ajpendo.1998.275.1.e94.

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We have previously described a temporal relationship between plasma cortisol pulses and slow-wave sleep and, more recently, an inverse significant cross-correlation between cortisol secretory rates and delta wave activity of the sleep electroencephalogram (EEG). The aim of this study was to observe ACTH, cortisol, and sleep delta wave activity variations throughout 24 h to get a better insight into their initiating mechanisms. Two groups of 10 subjects participated in a 24-h study, one group with a night sleep (2300–0700) and the other with a day sleep (0700–1500). Cortisol secretory rates were calculated by a deconvolution procedure from plasma levels measured at 10-min intervals. Delta wave activity was computed during sleep by spectral analysis of the sleep EEG. When delta waves and cortisol were present at the same time at the end of the night sleep as well as during the daytime sleep, they were negatively correlated, cortisol changes preceding variations in delta wave activity by ∼10 min. Increases in delta wave activity occurred in the absence of cortisol pulses, as observed at the beginning of the night. Cortisol pulses occurred without any concomitant variations of sleep delta wave activity, as observed during wakefulness and intrasleep awakenings. In no case did delta wave activity increase together with an increase in cortisol secretory rates. In conclusion, cortisol secretion and delta wave activity have independent generators. They can oscillate independently from each other, but when they are present at the same time, they are oscillating in phase opposition.

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Liang, Guo Zhuang, Jing Xia Wei, and Quan Min Zhu. "Power Spectrum Analysis of EEG before and after the 1/f Wave Electrical Stimulation." Applied Mechanics and Materials 303-306 (February 2013): 839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.839.

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Power spectrum analysis can make EEG which the amplitude changes with time transformation for spectrum chart which the EEG power changes with time. From the spectrum chart the distribution of the α-wave, the θ-wave, the δ-wave and the β-wave, and the change of rhythm can be observed directly. On this study, the 1/f wave had been applied on the treatment of patients with mental disease , the analysis and the research of EEG before and after the 1/f wave electrical stimulation. The results show that, the 1/f wave electric stimulation has a significant effect for mental disease patients which were caused by structural damage.

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Kim, Chang Seong, Yong-Hwi Kim, Seon-Gyu Choi, Kwang-Beom Ko, and Kyeong-Soo Han. "Mineral Identification and Field Application by Short Wave Infrared (SWIR) Spectroscopy." Economic and Environmental Geology 50, no. 1 (February 28, 2017): 1–14. http://dx.doi.org/10.9719/eeg.2017.50.1.1.

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Truong, Nhut Mai, Quoc Khai Le, and Quang Linh Huynh. "EEG – based study on sleep quality improvement by using music." Science & Technology Development Journal - Engineering and Technology 3, SI3 (December 2, 2020): First. http://dx.doi.org/10.32508/stdjet.v3isi3.670.

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Napping is essential for human to reduce drowsiness, contribute to improving cognitive function, reflex, short-term memory, and state. Some studies have shown that a certain amount of time for a nap can boost the body's immunity and reduce the danger of cardiovascular disease. Using music for relaxation and enjoyment to fall asleep is an effective solution that earlier studies have shown. There are many genres of music that have been used for stimulation, such as binaural beats or melodic sounds. The aim of the study was to confirm the positive effect of music on sleep quality by analyzing electroencephalography signal. There were four types of music is being used in this study: instrumental music, Ballad music, K-pop music, and Jazz. The study applied the pre-processing include filtering block, features extraction, and clustering steps to analyze raw data. This research calculated the power spectrum of Alpha wave and Theta wave, to detect the transition of wake - sleep stages by K-means clustering algorithm. Sleep latency is one of the factors that determine the quality of sleep. The sleep onset is detected based on the phase shift of the Alpha and Theta waves. The exact timing of the sleep onset was important in this study. The user interface was developed in this study to compute sleep latency in normal and musical experiment. As a result, music is an intervention in helping people fall asleep easier (mean of sleep latency in normal and musical experiment was 9.0714 min and 5.6423 min, respectively) but the standard deviation of this result was rather high due to the little number of experiments. However, the study concludes that listening to music before naptime can improve sleep latency in some participants.

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Veggiotti, Pierangelo, Francesca Beccaria, Renzo Guerrini, Giuseppe Capovilla, and Giovanni Lanzi. "Continuous Spike-and-Wave Activity During Slow-Wave Sleep: Syndrome or EEG Pattern?" Epilepsia 40, no. 11 (November 1999): 1593–601. http://dx.doi.org/10.1111/j.1528-1157.1999.tb02045.x.

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Abbasi, Hamid, Laura Bennet, Alistair J. Gunn, and Charles P. Unsworth. "Robust Wavelet Stabilized ‘Footprints of Uncertainty’ for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia." International Journal of Neural Systems 27, no. 03 (February 27, 2017): 1650051. http://dx.doi.org/10.1142/s0129065716500519.

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Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact ‘footprint of uncertainty’ (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30[Formula: see text]h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of [Formula: see text] for the clinical [Formula: see text] sampled EEG and [Formula: see text] for the high resolution [Formula: see text] sampled EEG that is improved upon over conventional standard wavelet [Formula: see text] and [Formula: see text], respectively, and fuzzy approaches [Formula: see text] and [Formula: see text], respectively, when performed in isolation.

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Yildirim, Sema, Hasan Erdinc Kocer, and Ahmet Hakan Ekmekci. "Quantitative Analysis of EEG Slow Wave Activity Based on MinPeakProminence Method." Traitement du Signal 38, no. 3 (June 30, 2021): 757–73. http://dx.doi.org/10.18280/ts.380323.

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Persistent, unchanging, and non-reactive focal or generalized abnormal Slow Wave (SW) activities in an awake adult patient are examined pathologically. Although these waves in Electroencephalogram (EEG) are much less prominent than transient activities in some areas, it is not possible to understand them easily by looking at the EEG. For this reason, reliable computer programs that can sort out Slow Waves (SWs) correctly are needed. In this study, a new method based on MinPeakProminence that can detect abnormal SW activities was developed. To test the performance of the study, the data collected from Selcuk University Hospital (22 subjects - epilepsy and various neurological diseases) and Bonn Hospital (only normal A dataset) were used. Various statistical performance measurement methods were used to search the results. The results of this analysis revealed that the classification success, sensitivity and specificity values obtained with the SUH dataset were 96.5%, 93.3% and 96.1%, respectively. In the results of the experiments made with the Bonn dataset, 100% classification success was achieved. Besides, according to the analyses, it was found that SWs are frequently seen in the posterior regions of the brain, especially in the parietal and occipital regions in the SUH dataset.

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Frank, M. G., and H. C. Heller. "Development of diurnal organization of EEG slow-wave activity and slow-wave sleep in the rat." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 273, no. 2 (August 1, 1997): R472—R478. http://dx.doi.org/10.1152/ajpregu.1997.273.2.r472.

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This study characterizes the development of diurnal patterns of slow-wave sleep (SWS) distribution and SWS electroencephalographic (EEG) delta-power (DP) density in 12- to 24-day-old rats (P12-P24). Diurnal organization in sleep-wake distribution was established by P20. A decline in SWS DP across the light phase did not appear until P24. Before P20, SWS DP increased across the light phase in a pattern inverse to that typically seen in adult rats. At P20, SWS DP was evenly distributed across the light phase, and at P24, SWS DP declined across the light phase. The transient dissociation between diurnal organization in sleep-wake cycles and SWS DP suggests that circadian and homeostatic sleep regulatory mechanisms develop at different rates in the postnatal period.

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Kenney, M. J., G. L. Gebber, S. M. Barman, and B. Kocsis. "Forebrain rhythm generators influence sympathetic activity in anesthetized cats." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 259, no. 3 (September 1, 1990): R572—R578. http://dx.doi.org/10.1152/ajpregu.1990.259.3.r572.

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Autospectral and coherence analyses were used to study the frequency-domain relationships between frontal-parietal cortical activity [electroencephalogram (EEG)] and the discharges of the interior cardiac and renal sympathetic nerves of baroreceptor-denervated and vagotomized cats anesthetized with either alpha-chloralose or pentobarbital sodium. Delta slow-wave activity in the EEG was correlated to sympathetic nerve discharge (SND) as shown by sharp peaks between 0.5 and 4 Hz in the coherence function. The relationship was stronger in chloralose- than in pentobarbital-anesthetized cats. Coherence of the two signals could be attributed to descending influences of forebrain delta slow-wave generators on sympathetic circuits, since midbrain transection preferentially reduced the power in SND at frequencies that cohered to the EEG before transection. In contrast, the power in the EEG was not reduced by midbrain transection. The relationship between cortical delta slow-wave activity and SND was stronger during than between cortical spindlelike events that lasted 1-3 s and recurred once every 5-10 s. These events were similar to cortical spindles observed during the early stages of sleep and under light barbiturate anesthesia. These observations raise the possibility that the influences of forebrain delta slow-wave generators on SND are gated by thalamic mechanisms normally involved in the sleep-wake cycle.

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Opp, M. R., F. Obal, and J. M. Krueger. "Interleukin 1 alters rat sleep: temporal and dose-related effects." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 260, no. 1 (January 1, 1991): R52—R58. http://dx.doi.org/10.1152/ajpregu.1991.260.1.r52.

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Rats received various doses of interleukin 1 (IL-1) (range, 0.5-25.0 ng) or pyrogen-free saline intracerebroventricularly during the rest (light) and the active (dark) cycles of the day, and sleep-wake activity and brain temperature were determined for 6 h. Low doses of IL-1 (0.5 ng at night, 2.5 ng during the day) increased both the duration of non-rapid-eye-movement sleep (NREMS) and electroencephalogram (EEG) slow-wave activity during NREMS episodes. Increasing doses of IL-1 had divergent effects on NREMS duration and EEG slow-wave activity, and the direction of the changes depended on the diurnal cycle. Thus NREMS duration was promoted at night and EEG slow-wave amplitudes during the day, whereas NREMS duration during the day and EEG slow-wave amplitudes at night were suppressed after higher doses of IL-1. High doses of IL-1 also induced decreases in rapid-eye-movement sleep during both phases of the day. Each dose of IL-1 that promoted NREMS also tended to increase brain temperature. These results demonstrate that IL-1 promotes NREMS in the rat. However, unlike previously reported findings in rabbits, the circadian rhythm of sleep regulation strongly interferes with the sleep-promoting activity of IL-1 in rats.

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Ogunyemi, Abayomi. "Triphasic Waves During Post-Ictal Stupor." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 23, no. 3 (August 1996): 208–12. http://dx.doi.org/10.1017/s0317167100038531.

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AbstractBackground: The term, “triphasic wave” originally described an EEG pattern believed to be a marker for a specific stage of hepatic coma. For 4 decades, the diagnostic and prognostic specificity of the pattern remains controversial. Its pathophysiology also continues to be elusive. Methods: EEG recordings were obtained in three patients known or suspected to have primary generalized epilepsy. In 2 patients, the EEGs were part of long-term monitoring using simultaneous video-EEG telemetry. For the third patient, the EEG was secured only during the post-ictal unconsciousness. These 3 patients were specifically selected because of the presence of triphasic waves in their EEGs. Results: Triphasic waves were observed in the EEG of the 3 patients only during post-ictal unconsciousness. The pattern was transient, being preceded by generalized suppression and delta slow waves and followed by theta activities. Alpha rhythms supervened when the patients became fully alert. Conclusion: A post-ictal state should be considered in unconscious patients with triphasic EEG waves.

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Oguni, Hirokazu. "Wearable EEG Device for Continuous Spike–Wave in Sleep." Pediatric Neurology Briefs 34 (December 2, 2020): 10. http://dx.doi.org/10.15844/pedneurbriefs-34-10.

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Baå-Eroglu, C., D. Strüber, M. Stadler, P. Kruse, and E. Baå. "Multistable Visual Perception Induces a Slow Positive EEG Wave." International Journal of Neuroscience 73, no. 1-2 (January 1993): 139–51. http://dx.doi.org/10.3109/00207459308987220.

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Lipschitz, Y. Meir, Liora Bernstein-Lipschitz, S. Flechter, and J. Vardi. "Malignant Cystic Meningioma with Spike and Wave EEG Pattern." Clinical Electroencephalography 19, no. 1 (January 1988): 20–25. http://dx.doi.org/10.1177/155005948801900107.

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Ferri, Raffaele, Filippa Alicata, Stefano Del Gracco, Maurizio Elia, Sebastiano A. Musumeci, and Maria C. Stefanini. "Chaotic behavior of EEG slow-wave activity during sleep." Electroencephalography and Clinical Neurophysiology 99, no. 6 (December 1996): 539–43. http://dx.doi.org/10.1016/s0013-4694(96)95719-3.

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SAITO, MASAKO, KEIKO KUREMOTO, KEIICHI TAKAHASHI, and SHINICHI NIIJIMA. "Clinical significance of sharp wave transients on neonatal EEG." Juntendo Medical Journal 53, no. 3 (2007): 428–37. http://dx.doi.org/10.14789/pjmj.53.428.

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FERRI, R., F. RUNDO, O. BRUNI, M. TERZANO, and C. STAM. "Dynamics of the EEG slow-wave synchronization during sleep." Clinical Neurophysiology 116, no. 12 (December 2005): 2783–95. http://dx.doi.org/10.1016/j.clinph.2005.08.013.

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H, Hakimi, M., Salleh, S. M, Ainul, H. M. Y, Ngali, M. Z, Ismail, A. E, Rahman, M. N. A, and Mahmud, W. M. A. W. "Ice Bath Therapy on Athletes Recovery Response Using EEG." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 438. http://dx.doi.org/10.14419/ijet.v7i4.30.22361.

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Sport recovery system is an integral aspect to help athletes adapt faster to training. This is an important process of physical preparation by reducing fatigue where the athletes can ready for the next competition or training. However, most of an athlete doing training without having the fully recovery after the training and can affect their performance. The cold bath water immersion is the one of common technique to recover from the fatigue. In this study, Neurosky mindwave is use to extract the brain wave of an athlete to know the response of an athlete when perform the cold water immersion. The responses of an athlete include meditation which is in alpha wave that state in relax condition and beta wave that is in fatigue condition in sport. The raw brain wave signal that extract using Neurosky mindwave is analysed using Matlab in terms of time domain. After that, Fast Fourier Transform (FFT) will use to analysed in terms of frequency domain. This project used alpha and beta band to collect the data. The analysis have made based on the peak value in frequency domain to know the best time for cold water immersion and best cold bath temperature.

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Shi, Yuan, DanDan He, and Fang Qin. "Study on Bayes Discriminant Analysis of EEG Data." Open Biomedical Engineering Journal 8, no. 1 (December 31, 2014): 142–46. http://dx.doi.org/10.2174/1874120701408010142.

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Objective: In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. Methods: In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results in use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Conclusions: Bayes Discriminant has higher prediction accuracy, EEG features (mainly α wave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.

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El Kanbi, K., V. Thorey, L. Artemis, A. Chouraki, T. Trichet, C. Pinaud, E. Debellemaniere, and P. J. Arnal. "0352 A Large-Scale EEG Study at Home to Objectivise Effects of Ageing on Slow Wave Sleep and Process S." Sleep 43, Supplement_1 (April 2020): A133—A134. http://dx.doi.org/10.1093/sleep/zsaa056.349.

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Abstract Introduction Several studies have shown slow wave sleep (SWS) is altered with ageing. However, most of these studies have been conducted in-lab and usually over a single night. In this study, we assessed the evolution of process S with ageing by analysing the dynamics of endogenous and auditory-evoked slow waves in a large population. Methods 300 participants (200 M, 20 - 70 y.o.) were selected from volunteers users wearing a sleep headband for at least 3 nights, meeting the criteria of high signal quality and having no subjective sleep complaints nor being shift-workers. The Dreem headband is a connected device able to monitor EEG signals as well as pulse and movement and performs sleep staging in real-time automatically. Slow waves were detected as large negative deflections on the filtered EEG signals during NREM sleep. The auditory evoked slow waves were done using a previously validated closed-loop procedure. Results In our study, age was strongly correlated with N3 sleep duration (r=-0.34, p<0.0001), slow wave amplitude (r=-0.25, p<0.0001), and slow wave density (r=-0.40, p<0.0001). The slope of the slow wave activity, representing the process S here, was significantly decreased (r=-0.32, p<0.0001). This effect was mainly due to changes in the density of slow waves in the first 2 hours of sleep (r=-0.41, p<0.0001). Finally, our results show a decrease in the probability of auditory evoked slow waves (r=-0.43, p<0.0001). Conclusion These results confirmed the in-lab studies showing a heterogeneous alteration of homoeostatic process S with age, as well as a general decrease of slow wave occurrences, that is observed in parallel of a decrease of the probability of evoking slow waves, suggesting a global change in the system responsible for slow wave generation. Support This study was supported by Dreem sas and ANR, FLAG ERA 2015, HPB SLOW-Dyn

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Danilenko, Konstantin V., Evgenii Kobelev, Sergei V. Yarosh, Grigorii R. Khazankin, Ivan V. Brack, Polina V. Miroshnikova, and Lyubomir I. Aftanas. "Effectiveness of Visual vs. Acoustic Closed-Loop Stimulation on EEG Power Density during NREM Sleep in Humans." Clocks & Sleep 2, no. 2 (April 30, 2020): 172–81. http://dx.doi.org/10.3390/clockssleep2020014.

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The aim of the study was to investigate whether visual stimuli have the same potency to increase electroencephalography (EEG) delta wave power density during non-rapid eye movement (NREM) sleep as do auditory stimuli that may be practical in the treatment of some sleep disturbances. Nine healthy subjects underwent two polysomnography sessions—adaptation and experimental—with EEG electrodes positioned at Fz–Cz. Individually adjusted auditory (pink noise) and visual (light-emitting diode (LED) red light) paired 50-ms signals were automatically presented via headphones/eye mask during NREM sleep, shortly (0.75–0.90 s) after the EEG wave descended below a preset amplitude threshold (closed-loop in-phase stimulation). The alternately repeated 30-s epochs with stimuli of a given modality (light, sound, or light and sound simultaneously) were preceded and followed by 30-s epochs without stimulation. The number of artifact-free 1.5-min cycles taken in the analysis was such that the cycles with stimuli of different modalities were matched by number of stimuli presented. Acoustic stimuli caused an increase (p < 0.01) of EEG power density in the frequency band 0.5–3.0 Hz (slow waves); the values reverted to baseline at post-stimuli epochs. Light stimuli did not influence EEG slow wave power density (p > 0.01) and did not add to the acoustic stimuli effects. Thus, dim red light presented in a closed-loop in-phase fashion did not influence EEG power density during nocturnal sleep.

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Zhang, Zheng Xia, Si Qiu Xu, Er Ning Zhou, Xiao Lin Huang, and Jun Wang. "Multiscale Jensen-Shannon Divergence Based Analysis of Beta Wave Attention EEG." Applied Mechanics and Materials 574 (July 2014): 723–27. http://dx.doi.org/10.4028/www.scientific.net/amm.574.723.

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The article adopted the multiscale Jensen - Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states of Beta EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze),we are aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data, finding that there are large amounts of nonlinear time series in the Beta EEG signals. This method is also fully proved that the multi-scale JSD algorithm can be used to analyze EEG signals, attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

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Lal, Saroj K. L., and Ashley Craig. "Electroencephalography Activity Associated with Driver Fatigue: Implications for a Fatigue Countermeasure Device." Journal of Psychophysiology 15, no. 3 (July 2001): 183–89. http://dx.doi.org/10.1027//0269-8803.15.3.183.

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Abstract This paper reviews the association between electroencephalography (EEG) activity and driver fatigue. The current literature shows substantial evidence of changes in brain wave activity, such as simultaneous changes in slow-wave activity (e.g., delta and theta activity) as well as alpha activity during driver fatigue. It is apparent from the literature review that EEG is a promising neurophysiological indicator of driver fatigue and has the potential to be incorporated into the development of a fatigue countermeasure device. The findings from this review are discussed in the light of directions for future fatigue research studies.

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