Ahmad Rauf Subhani

Association of Autonomic Nervous System and EEG Scalp Potential During Playing 2D Grand Turismo 5

Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p<;0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p<;0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

Dynamics of Scalp Potential and Autonomic Nerve Activity during Intelligence Test

The main objective of this study was to examine the changes in autonomic nervous system (ANS) and scalp potential during intelligence test (IQ). Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were recorded simultaneously from eight healthy participants during IQ and resting states (eyes–closed and eyes-open). Heart rate (HR) and heart rate variability (HRV) were derived from ECG signal. EEG mean power was computed for five frequency bands (delta, theta, alpha, beta, and gamma) and analyzed in 12 regions across the scalp. The EEG frequency bands showed significant (p<0.025) changes between IQ test and rest states. Delta and theta at frontal (PF, AF, F) and temporal regions (FT, T, TP) and alpha activity at parietal (P), parieto-occipital (PO) and occipital (O) regions were significant. In beta and gamma bands, highly reduced mean power was found at P, PO, and O regions as compared to PF, AF, and F regions in IQ test. HR and low frequency in normalized unit (LFnu) were increased significantly (p<0.05 and p<0.025, respectively) in IQ test. Further, high frequency in normalized unit (HFnu) was decreased (p<0.11). Results showed parallel changes in scalp potential and automatic nervous activity during IQ test compared to rest conditions.

Mitigation of stress: new treatment alternatives

Complaints of stress are common in modern life. Psychological stress is a major cause of lifestyle-related issues, contributing to poor quality of life. Chronic stress impedes brain function, causing impairment of many executive functions, including working memory, decision making and attentional control. The current study sought to describe newly developed stress mitigation techniques, and their influence on autonomic and endocrine functions. The literature search revealed that the most frequently studied technique for stress mitigation was biofeedback (BFB). However, evidence suggests that neurofeedback (NFB) and noninvasive brain stimulation (NIBS) could potentially provide appropriate approaches. We found that recent studies of BFB methods have typically used measures of heart rate variability, respiration and skin conductance. In contrast, studies of NFB methods have typically utilized neurocomputation techniques employing electroencephalography, functional magnetic resonance imaging and near infrared spectroscopy. NIBS studies have typically utilized transcranial direct current stimulation methods. Mitigation of stress is a challenging but important research target for improving quality of life.

Tracking of EEG Activity Using Motion Estimation to Understand Brain Wiring

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Machine Learning Framework for the Detection of Mental Stress at Multiple Levels

Mental stress has become a social issue and could become a cause of functional disability during routine work. In addition, chronic stress could implicate several psychophysiological disorders. For example, stress increases the likelihood of depression, stroke, heart attack, and cardiac arrest. The latest neuroscience reveals that the human brain is the primary target of mental stress, because the perception of the human brain determines a situation that is threatening and stressful. In this context, an objective measure for identifying the levels of stress while considering the human brain could considerably improve the associated harmful effects. Therefore, in this paper, a machine learning (ML) framework involving electroencephalogram (EEG) signal analysis of stressed participants is proposed. In the experimental setting, stress was induced by adopting a well-known experimental paradigm based on the montreal imaging stress task. The induction of stress was validated by the task performance and subjective feedback. The proposed ML framework involved EEG feature extraction, feature selection (receiver operating characteristic curve, t-test and the Bhattacharya distance), classification (logistic regression, support vector machine and naïve Bayes classifiers) and tenfold cross validation. The results showed that the proposed framework produced 94.6% accuracy for two-level identification of stress and 83.4% accuracy for multiple level identification. In conclusion, the proposed EEG-based ML framework has the potential to quantify stress objectively into multiple levels. The proposed method could help in developing a computer-aided diagnostic tool for stress detection.

Using resting state coherence to distinguish between low and high stress groups

Mental stress that is originated due to high task demands affects our life. Human brain is a target of stress. Neuronal variations take place in the brain and make many brain regions communicate with each other to process the information flow. Electroencephalographic (EEG) coherence is a mathematical representation of cross talk between two brain regions. This paper aims to explore the irregularities in EEG coherence due to the exposure of mental stress. Furthermore, this paper also explores the difference in brain connectivity between low-stress and high-stress subjects. Twenty-two subjects were exposed to a stressful situation for twenty minutes. Their EEG was recorded and compared in pre- and post-stress rest conditions to mark irregularities in EEG coherence. The distinction of subjects in low- and high-stress was done based on their score in the Perceived Stress Scale (PSS).

Difference in brain dynamics during arithmetic task performed in stress and control conditions

This paper highlights the difference in brain dynamics in terms of activity and connectivity of the brain between stress and control conditions. In a cross-over experimental design, 22 subjects participated to perform arithmetic task under stress and control environments in which electroencephalogram (EEG) data were recorded. Prior to arithmetic task in both conditions, EEG data were recorded in eyes-open rest condition to be used as baseline. Mean absolute power and coherence of EEG scalp potentials in delta, theta, alpha and beta bands were measured and used as activity and connectivity of the brain. Results show an overall increased activation during stress and control conditions. Between the conditions, the significant difference in power was found in beta band showing difference in parietal region. The coherence maps generally showed hypercoherence in every band between two conditions.

A physiological signal-based method for early mental-stress detection

Abstract The early detection of mental stress is critical for efficient clinical treatment. As compared with traditional approaches, the automatic methods presented in literature have shown significance and effectiveness in terms of diagnosis speed. Unfortunately, the majority of them mainly focus on accuracy rather than predictions for treatment efficacy. This may result in the development of methods that are less robust and accurate, which is unsuitable for clinical purposes. In this study, we propose a comprehensive framework for the early detection of mental stress by analysing variations in both electroencephalogram (EEG) and electrocardiogram (ECG) signals from 22 male subjects (mean age: 22.54 ± 1.53 years). The significant contribution of this paper is that the presented framework is capable of performing predictions for treatment efficacy, which is achieved by defining four stress levels and creating models for the individual level. The experimental results indicate that the framework has realised an accuracy, a sensitivity, and a specificity of 79.54%, 81%, and 78%, respectively. Moreover, the results indicate significant neurophysiological differences between the stress and control (stress-free) conditions at the individual level.