Technical Aspects of Brain Rhythms and Speech Parameters

Abhang, Priyanka A.; Gawali, Bharti W.; Mehrotra, S. C.

doi:10.1016/b978-0-12-804490-2.00003-8

Cited by 62 publications

(47 citation statements)

References 9 publications

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“…Converging evidence from animal and human research suggests that synchronization in the gamma band is important for neural communication and, in particular, for neural network processes that underpin attention, working memory, and long-term memory (Jensen et al, 2007). In addition, prominent gamma waves are associated with states of high arousal, anxiety, and stress (Abhang et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Radmanesh

Jalili

Kozlowska

2020

Front. Hum. Neurosci.

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Objectives: Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls. Methods: The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method. Results: Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics. Conclusion: The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.

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Section: Discussionmentioning

confidence: 99%

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Radmanesh

Jalili

Kozlowska

2020

Front. Hum. Neurosci.

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“…For example, the beta band can be divided in low and high beta activity. Although low beta (12–15 Hz) is mostly associated with quiet, focused, and introverted concentration, high beta (21–32 Hz) is commonly related to significant stress, anxiety, paranoia, high energy, and high arousal (Abhang, Gawali, & Mehrotra, ; Demos, ). Clinically, abnormalities in the beta band are characteristic for individuals with impulse regulation disorders (e.g., substance use disorders; Knott, Cosgrove, et al, ; Knott, Naccache, et al, ; Littel, Franken, & Van Strien, ; Liu, Vaupel, Grant, & London, ; Reid et al, ).…”

Section: Introductionmentioning

confidence: 99%

Abnormalities in the EEG power spectrum in bulimia nervosa, binge‐eating disorder, and obesity: A systematic review

Blume

Schmidt

Hilbert

2018

Euro Eating Disorders Rev

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To provide a basis for electroencephalography (EEG) neurofeedback protocols for bulimia nervosa (BN), binge‐eating disorder (BED), and obesity, this systematic review investigates alterations in EEG‐measured brain activity, specifically frequency bands. A systematic literature search with predefined search terms yielded N = 7 studies meeting the inclusion criteria. The risk of bias was assessed for all studies. In resting‐state EEG, the beta activity was elevated in fronto‐central regions in individuals with obesity and co‐morbid BED. In food‐cue conditions, both obese individuals with and without BED showed increased beta activity, suggesting increased awareness of food cues and a heightened attentional focus towards food stimuli. The level of beta activity was positively correlated with eating disorder psychopathology in resting and food‐cue conditions. In individuals with BN, there was no evidence for altered EEG spectral power. The results indicate specific alterations in EEG‐based brain activity in individuals with BED and obesity. More high‐quality studies are needed to further confirm these findings and to transfer them into EEG‐based interventions.

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“…During memorization tasks, the activation of the gamma waves is visible in the temporal lobe. The predominance of these waves has been associated with the installation of anxiety, stress, or arousal states [32].…”

Section: Biophysical Datamentioning

confidence: 99%

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

et al. 2019

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Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.In the 1970s, Paul Ekman identified six basic emotions [2], namely anger, disgust, fear, joy, sadness, and surprise. Russell and Mehrabian proposed a dimensional approach [3] which states that any emotion is represented relative to three fundamental dimensions, namely valence (positive/pleasurable or negative/unpleasurable), arousal (engaged or not engaged), and dominance (degree of control that a person has over their affective states).Joy or happiness is a pleasant emotional state, synonymous with contentment, satisfaction and well-being. Sadness is the opposite of happiness, being characterized by grief, disappointment, and distress. Fear emerges in the presence of a stressful or dangerous stimulus perceived by the sensory organs. When the fight or flight response appears, heart rate and respiration rate increase. Also, the muscles become more tense in order to contend with threats in the environment. Anger is defined by fury, frustration, and resentment towards others. Surprise is triggered by an unexpected outcome to a situation, ranging from amazement to shock, whereas disgust is synonymous with dislike, distaste, or repugnance, being the most visceral of all six emotions.The DEAP database [4] was created with the purpose of developing a music video recommendation system based on the users' emotional responses. The biophysical signals of 32 subjects have been recorded while they were watching 40 one-minute long excerpts of music videos eliciting various emotions. The participants rated each video in terms of valence, arousal, dominance, like/dislike and familiarity on a scale from one to nine. The physiological signals were: galvanic skin response (GSR), plethysmograph (PP...

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Technical Aspects of Brain Rhythms and Speech Parameters

Cited by 62 publications

References 9 publications

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Abnormalities in the EEG power spectrum in bulimia nervosa, binge‐eating disorder, and obesity: A systematic review

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

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