Ali Motie Nasrabadi

Bio: Ali Motie Nasrabadi is an academic researcher from Shahed University. The author has contributed to research in topics: Epilepsy & Feature extraction. The author has an hindex of 19, co-authored 174 publications receiving 1514 citations. Previous affiliations of Ali Motie Nasrabadi include Amirkabir University of Technology & Islamic Azad University.

Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving

Abstract: BACKGROUND: Driver fatigue is one of the major implications in transportation safety and accounted for up to 40% of road accidents. This study aimed to analyze the EEG alpha power changes in partially sleep-deprived drivers while performing a simulated driving task. METHODS: Twelve healthy male car drivers participated in an overnight study. Continuous EEG and EOG records were taken during driving on a virtual reality simulator on a monotonous road. Simultaneously, video recordings from the driver face and behavior were performed in lateral and front views and rated by two trained observers. Moreover, the subjective self-assessment of fatigue was implemented in every 10-min interval during the driving using Fatigue Visual Analog Scale (F-VAS). Power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. RESULTS: The findings showed a significant increase in the absolute alpha power (P = 0.006) as well as F-VAS scores during the final section of driving (P = 0.001). Meanwhile, video ratings were consistent with subjective self-assessment of fatigue. CONCLUSION: The increase in alpha power in the final section of driving indicates the decrease in the level of alertness and attention and the onset of fatigue, which was consistent with F-VAS and video ratings. The study suggested that variations in alpha power could be a good indicator for driver mental fatigue, but for using as a countermeasure device needed further investigations. Language: en

Determining fault's type and accurate location in distribution systems with DG using MLP Neural networks

Abstract: Finding and designing new methods for determining type and exact location of faults in power system has been a major subject for power system protection engineers in recent years. Fault locating in transmission networks is not very hard and complicated due to low impedance of faults. This job is usually done by distance relays. But, in distribution networks, because of high impedance of fault and its vast variety and also simplicity of protective devices, determining the exact location of faults is very complicated. On the other hand, penetration of distribution generation into distribution networks reinforces the necessity of designing new protection systems for these networks. One of the main capabilities that can improve the efficiency of new protection relays in distribution systems is exact fault locating. In this paper, a new approach for determining the exact fault type and location in distribution systems including distributed generation using MLP neural networks is presented. In the suggested method, after determining the fault type, by normalizing the fault current of the main source, the corresponding trained neural network has been activated and the exact location of occurred fault has been derived. The presented method has been implemented on a sample distribution network, simulated by DIgSILENT Power Factory 13.2, and its performance has been tested. The simulation results show high performance and accuracy of the method and substantiate that it can be used in modern heuristic protection schemes in distribution systems.

A Review on EEG Signals Based Emotion Recognition

Abstract: Emotion recognition has become a very controversial issue in brain-computer interfaces (BCIs). Moreover, numerous studies have been conducted in order to recognize emotions. Also, there are several important definitions and theories about human emotions. In this paper we try to cover important topics related to the field of emotion recognition. We review several studies which are based on analyzing electroencephalogram (EEG) signals as a biological marker in emotion changes. Considering low cost, good time and spatial resolution, EEG has become very common and is widely used in most BCI applications and studies. First, we state some theories and basic definitions related to emotions. Then some important steps of an emotion recognition system like different kinds of biologic measurements (EEG, electrocardiogram [EEG], respiration rate, etc), offline vs online recognition methods, emotion stimulation types and common emotion models are described. Finally, the recent and most important studies are reviewed.

A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal

Abstract: Over the past decade, converging evidence from diverse studies has demonstrated that sleep is closely associated with the mental and physical health, quality of life, and safety. Visual sleep scoring provides an initial and tangible illustration of how the brain wave changes across different sleep stages. The main objective of the present study is to design an accurate and robust computer-assisted sleep stage scoring system using single-channel EEG signal by proposing a novel time domain feature named Statistical Behavior of Local Extrema (SBLE). SBLE provides a profound understanding of hidden dynamics of EEG signals by quantifying and symbolizing its local extrema information, extracting and defining various patterns, and statistical analysis of extracted patterns. First, each EEG segment was decomposed into 6 frequency sub-bands (i.e., low-delta, high-delta, theta, alpha, sigma, and beta). Next, SBLE features were separately computed from each sub-band. Then, an optimal feature set with a high rate of accuracy was selected using a supervised Multi-Cluster/Class Feature Selection (MCFS) algorithm. Finally, the selected features were fed to a multi-class Support Vector Machine (SVM) for classification purposes. The benchmark Sleep-EDF dataset and DREAMS Subject Database were employed to evaluate the performance of the proposed framework. The average (± variance) accuracy rates were 90.6 ± 4.2%, 91.8 ± 5.0%, 92.8 ± 3.3%, 94.5 ± 3.4%, 97.9 ± 1.4% for six-stage to two-stage sleep classification on Sleep-EDF dataset, respectively. Besides, its performance on DREAMS Subjects Database was also promising in term of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. Experimental results suggest that the proposed methodology can precisely solve the multi-class sleep stage classification problem by presenting an innovative symbolic approach similar to physician's point of view.

The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalogram signals.

Abstract: Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies-Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies-Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

An Overview of Heart Rate Variability Metrics and Norms.

Abstract: Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.

A review of classification algorithms for EEG-based brain–computer interfaces

Abstract: In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

"Cognitive, social, and physiological determinants of emotional state": Erratum

Abstract: The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

The Analysis of Time Series: An Introduction

Abstract: The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.