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Electroencephalogram and EEG

Qingshan She, Kang Chen, Yuliang Ma, Thinh Nguyen, Yingchun Zhang
Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation...
2018: Computational Intelligence and Neuroscience
Susanne Koch, Claudia Spies
PURPOSE OF REVIEW: To summarize recent recommendations on intraoperative electroencephalogram (EEG) neuromonitoring in the elderly aimed at the prevention of postoperative delirium and long-term neurocognitive decline. We discuss recent perioperative EEG investigations relating to aging and cognitive dysfunction, and their implications on intraoperative EEG neuromonitoring in elderly patients. RECENT FINDINGS: The incidence of postoperative delirium in elderly can be reduced by monitoring depth of anesthesia, using an index number (0-100) derived from processed frontal EEG readings...
November 29, 2018: Current Opinion in Anaesthesiology
Bashkim Kadriu, Wen Gu, Panagiota Korenis, Jeffrey M Levine
BACKGROUND: Numerous studies shown that structural hippocampal alterations are present in subjects at high risk of developing psychosis or schizophrenia. These findings indicate that in a subset of patients undergoing first-psychosis episode (FPE), hippocampal volume alterations are accompanied by associated cognitive and neuropsychological deficits. The combination of psychological deficits and neuroanatomical alterations, in turn, appears to increase treatment complexity and worsen clinical outcomes...
December 3, 2018: CNS Spectrums
Scott Tillem, Grace Brennan, Jia Wu, Linda Mayes, Arielle Baskin-Sommers
Psychopathy is a personality disorder associated with callous, impulsive, and antisocial behaviors. Decades of research indicate that individuals higher on psychopathy exhibit abnormal allocation of attention during goal pursuit. However, the manner in which attention is allocated to goal-relevant information and the downstream neurocognitive consequences of this attention abnormality remain unclear. The present study addresses this gap by examining the relationship between psychopathy and the allocation of attention during an electroencephalogram (EEG)-based continuous performance task in a sample of 61 adolescents and young adults...
December 3, 2018: Personality Disorders
Tess S Fotidzis, Heechun Moon, Jessica R Steele, Cyrille L Magne
Recent evidence suggests the existence of shared neural resources for rhythm processing in language and music. Such overlaps could be the basis of the facilitating effect of regular musical rhythm on spoken word processing previously reported for typical children and adults, as well as adults with Parkinson's disease and children with developmental language disorders. The present study builds upon these previous findings by examining whether non-linguistic rhythmic priming also influences visual word processing, and the extent to which such cross-modal priming effect of rhythm is related to individual differences in musical aptitude and reading skills...
November 29, 2018: Brain Sciences
Sanchai Kuladee, Thanavadee Prachason, Porntip Srisopit, Dussanee Trakulchang, Apisit Boongird, Pattarabhorn Wisajan, Sudawan Jullagate
BACKGROUND: Many studies have shown that the prevalence of psychiatric disorders in patients with epilepsy (PWE) appears higher than that in general population. However, most epidemiological studies regarding psychiatric comorbidities among PWE were conducted in Western countries. This work aimed to determine the prevalence of psychiatric disorders in Thai PWE, including potential variables that could be associated with psychiatric disorders. METHODS: A cross-sectional study was conducted at Ramathibodi Hospital...
November 27, 2018: Epilepsy & Behavior: E&B
Mathieu Schertz, Benzakoun Joseph, Pyatigorskaya Nadya, Samia Belkacem, Sahli-Amor Melika, Navarro Vincent, Cholet Clément, Leclercq Delphine, Dormont Didier, Law-Ye Bruno
PURPOSE: Arterial spin labeling (ASL) is a noninvasive tool measuring cerebral blood flow (CBF) and is useful to assess acute neurological deficit. While acute stroke presents as hypoperfused vascular territory, epileptic activity causes cortical hyperperfusion. Other neurological conditions exhibit hyperperfusion, like migraine or secondary "luxury perfusion" in strokes. Our objectives were to evaluate the usefulness and potential specificities of ASL in acute seizure and correlate it with electroencephalogram...
November 27, 2018: Journal of Neuroradiology. Journal de Neuroradiologie
Yijun Zou, Xingang Zhao, Yaqi Chu, Yiwen Zhao, Weiliang Xu, Jianda Han
A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target's EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods...
November 29, 2018: Medical & Biological Engineering & Computing
Iulia M Comsa, Tristan A Bekinschtein, Srivas Chennu
As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes...
November 29, 2018: Brain Topography
Julie Chi Chow, Chen-Sen Ouyang, Chin-Ling Tsai, Ching-Tai Chiang, Rei-Cheng Yang, Rong-Ching Wu, Hui-Chuan Wu, Lung-Chang Lin
Diagnosis of attention-deficit hyperactivity disorder (ADHD) is currently based on core symptoms or checklists; however, the inevitability of practitioner subjectivity leads to over- and underdiagnosis. Although the Federal Drug Administration has approved an elevated theta/beta ratio (TBR) of the electroencephalogram (EEG) band as a tool for assisting ADHD diagnosis, several studies have reported no significant differences of the TBR between ADHD and control subjects. This study detailed the development of a method based on approximate entropy (ApEn) analysis of EEG to compare ADHD and control groups...
November 29, 2018: Clinical EEG and Neuroscience: Official Journal of the EEG and Clinical Neuroscience Society (ENCS)
Chun-Hao Wang, David Moreau, Cheng-Ta Yang, Jui-Tang Lin, Yun-Yen Tsai, Chia-Liang Tsai
OBJECTIVE: Extensive evidence has demonstrated the relationship between aerobic fitness and cognitive function in early adulthood. Little is known, however, about whether the cognitive benefits of aerobic fitness are related to the modulation of top-down or bottom-up mechanisms in the control of executive attention. The present study aimed to shed light on this question by evaluating the phase-locking factor (PLF) of electroencephalogram (EEG) signal during cognitive control. METHOD: Higher fit and lower fit young adults performed a neuropsychological test of cognitive control (i...
November 29, 2018: Neuropsychology
Ruolei Gu, Jing Yang, Ziyan Yang, Zihang Huang, Mingzheng Wu, Huajian Cai
We proposed that self-affirmation can endow people with more cognitive resource to cope with uncertainty. We tested this possibility with an event-related potential (ERP) study by examining how self-affirmation influences ambiguous feedback processing in a simple gambling task, which was used to investigate risk decision-making. We assigned 48 participants randomly to the affirmation and non-affirmation (i.e., control) groups. All participants accepted the manipulation first and then completed the gambling task with an electroencephalogram (EEG) recording, in which participants might receive a positive (winning), negative (losing), or ambiguous (unknown valence) outcome after they made a choice...
November 28, 2018: Cognitive, Affective & Behavioral Neuroscience
Jichi Chen, Hong Wang, Chengcheng Hua, Qiaoxiu Wang, Chong Liu
A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states...
December 2018: Cognitive Neurodynamics
K G van Leeuwen, H Sun, M Tabaeizadeh, A F Struck, M J A M van Putten, M B Westover
OBJECTIVES: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors...
November 17, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
AbdelKebir Sabil, Jade Vanbuis, Guillaume Baffet, Mathieu Feuilloy, Marc Le Vaillant, Nicole Meslier, Frédéric Gagnadoux
Polysomnography (PSG) is necessary for the accurate estimation of total sleep time (TST) and the calculation of the apnea-hypopnea index (AHI). In type III home sleep apnea testing (HSAT), TST is overestimated because of the lack of electrophysiological sleep recordings. The aim of this study was to evaluate the accuracy and reliability of a novel automated sleep/wake scoring algorithm combining a single electroencephalogram (EEG) channel with actimetry and HSAT signals. The study included 160 patients investigated by PSG for suspected obstructive sleep apnea (OSA)...
November 26, 2018: Journal of Sleep Research
George S Plummer, Reine Ibala, Eunice Hahm, Jingzhi An, Jacob Gitlin, Hao Deng, Kenneth T Shelton, Ken Solt, Jason Z Qu, Oluwaseun Akeju
OBJECTIVE: Electroencephalogram burst-suppression during general anesthesia is associated with post-operative delirium (POD). Whether burst-suppression causes POD or merely reflects susceptibility to POD is unclear. We hypothesized decreased intraoperative alpha (8-12 Hz) and beta (13-33 Hz) power prior to the occurrence of burst-suppression in susceptible patients. METHODS: We analyzed intraoperative electroencephalogram data of cardiac surgical patients undergoing cardiopulmonary bypass (CPB)...
November 16, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
Boris Wagenseil, Carmen Garcia, Alexander V Suvorov, Ingo Fietze, Thomas Penzel
OBJECTIVE: Cranial electrotherapy stimulation (CES) is considered to be a potential treatment for insomnia. Women are more likely to suffer from insomnia than men. Therefore we studied the effect of CES on sleep efficiency in young healthy women. METHODS: A randomized, controlled clinical study was conducted on 40 women (age 18-35 years) without sleep disorders. Each subject underwent two nights of polysomnography in a sleep center. During the second night, we applied CES with a commercial device (Alpha-Stim 100) using either active or sham stimulation (double-blinded)...
November 26, 2018: Physiological Measurement
Zhimin Zhang, Shoushui Wei, Guohun Zhu, Feifei Liu, Yuwen Li, Xiaotong Dong, Chengyu Liu, Feng Liu
OBJECTIVE: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method based on entropy features and a support vector machine classifier, named SC-En&SVM. APPROACH: Entropy features, including fuzzy measure entropy (FuzzMEn), fuzzy entropy, and sample entropy are applied for the analysis and classification of sleep stages...
November 26, 2018: Physiological Measurement
Hillary K Schiltz, Alana J McVey, Alexander Barrington, Angela D Haendel, Bridget K Dolan, Kirsten S Willar, Sheryl Pleiss, Jeffrey S Karst, Elisabeth Vogt, Christina C Murphy, Kelsey Gonring, Amy Vaughan Van Hecke
The Modifier Model of autism spectrum disorder (ASD) suggests that phenotypic variability within ASD is rooted in modifier processes, such as the behavioral inhibition system (BIS) and behavioral activation system (BAS). Among a sample of 53 adolescents with ASD, this study examined associations between (a) self-reported BIS/BAS and frontal and parietal alpha electroencephalogram asymmetry and whether these indices related to (b) ASD severity (via the Autism Quotient), and/or (c) co-occurring anxiety and attention-deficit hyperactivity disorder (via Youth Self Report and Child Behavior Checklist)...
November 26, 2018: Autism Research: Official Journal of the International Society for Autism Research
Ramy Hussein, Hamid Palangi, Rabab K Ward, Z Jane Wang
OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures...
November 15, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
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