scholarly journals Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Noor Kamal Al-Qazzaz ◽  
Mohannad K. Sabir ◽  
Sawal Hamid Bin Mohd Ali ◽  
Siti Anom Ahmad ◽  
Karl Grammer
Keyword(s):  
Gender Differences ◽  
Feature Fusion ◽  
Permutation Entropy ◽  
Support Vector ◽  
Gender Recognition ◽  
Emotional States ◽  
The Novel ◽  
Svm Classification ◽  
Audiovisual Stimuli ◽  
The Brain

Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent Hur and amplitude-aware permutation entropy AAPE features were extracted from the EEG dataset. k -nearest neighbors kNN and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs. The main novelty of this paper is twofold: first, to investigate Hur as a complexity feature and AAPE as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA) and then integrating these features to propose a new CompEn hybrid feature fusion method towards developing the novel WT _ CompEn gender recognition framework as a core for an automated gender recognition model to be sensitive for identifying gender roles in the brain-emotion relationship for females and males. The results illustrated the effectiveness of Hur and AAPE features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed WT _ CompEn framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel WT _ CompEn may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel WT _ CompEn framework is a crucial goal for improving the process of automatic gender recognition from emotional-based EEG signals allowing for more comprehensive insights to understand various gender differences and human behavior effects of an intervention on the brain.

scholarly journals A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results

2019 ◽  
Vol 63 (3) ◽  
pp. 425-434 ◽  
Author(s):  
Negin Manshouri ◽  
Temel Kayikcioglu
Keyword(s):  
3D Video ◽  
Support Vector ◽  
Least Squares Regression ◽  
Eeg Signals ◽  
3D Technology ◽  
Svm Classification ◽  
Brain Signals ◽  
Short Period ◽  
The Impact ◽  
The Brain

Abstract Despite the development of two- and three-dimensional (2D&3D) technology, it has attracted the attention of researchers in recent years. This research is done to reveal the detailed effects of 2D in comparison with 3D technology on the human brain waves. The impact of 2D&3D video watching using electroencephalography (EEG) brain signals is studied. A group of eight healthy volunteers with the average age of 31 ± 3.06 years old participated in this three-stage test. EEG signal recording consisted of three stages: After a bit of relaxation (a), a 2D video was displayed (b), the recording of the signal continued for a short period of time as rest (c), and finally the trial ended. Exactly the same steps were repeated for the 3D video. Power spectrum density (PSD) based on short time Fourier transform (STFT) was used to analyze the brain signals of 2D&3D video viewers. After testing all the EEG frequency bands, delta and theta were extracted as the features. Partial least squares regression (PLSR) and Support vector machine (SVM) classification algorithms were considered in order to classify EEG signals obtained as the result of 2D&3D video watching. Successful classification results were obtained by selecting the correct combinations of effective channels representing the brain regions.

ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740009
Author(s):  
G. MURALIDHAR BAIRY ◽  
U. C. NIRANJAN ◽  
SHU LIH OH ◽  
JOEL E. W. KOH ◽  
VIDYA K. SUDARSHAN ◽  
...  
Keyword(s):  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Linear Features ◽  
Entropy Formula ◽  
Non Linear ◽  
The Brain

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.

Detection of Music-Induced Emotion Changes by Functional Brain Networks

2018 ◽  
pp. 155-177
Author(s):  
Reshma Kar ◽  
Amit Konar ◽  
Aruna Chakraborty
Keyword(s):  
Brain Connectivity ◽  
Brain Network ◽  
Relative Strength ◽  
Support Vector ◽  
Strength Increase ◽  
Emotional States ◽  
Statistical Measures ◽  
Vector Machines ◽  
Functional Brain Networks ◽  
The Brain

This chapter discusses emotions induced by music and attempts to detect emotional states based on regional interactions within the brain. The brain network theory largely attributes statistical measures of interdependence as indicators of brain region interactions/connectivity. In this paper, the authors studied two bivariate models of brain connectivity and employed thresholding based on relative values among electrode pairs, in order to give a multivariate flavor to these models. The experimental results suggest that thresholding the brain connectivity measures based on their relative strength increase classification accuracy by approximately 10% and 8% in time domain and frequency domain respectively. The results are based on emotion recognition accuracy obtained by decision tree based linear support vector machines, considering the thresholded connectivity measures as features. The emotions were categorized as fear, happiness, sadness, and relaxation.

Individual Differences in Masturbation and Erotica Consumption Predicting Levels of Sexual Concordance

2018 ◽  
Author(s):  
Graham Hutchings
Keyword(s):  
Gender Differences ◽  
Sexual Arousal ◽  
Sexual Experience ◽  
Cognitive Model ◽  
Sexual History ◽  
Emotional States ◽  
Erotic Stimuli ◽  
Subjective Sexual Arousal ◽  
Before And After ◽  
Audiovisual Stimuli

Concordance is not the only aspect of sexuality where significant gender differences are observable: men masturbate significantly more than women (Oliver & Hyde, 1993; Petersen & Hyde, 2011). There are also large gender differences in pornography consumption and consumption patterns (Hald, 2006). The study of concordance is important as it could assist in the further development of models of sexual response, and potentially reveal the role of gender differences in those models. Evidence suggests that the cognitive system one uses to process stimuli can affect one's subjective sexual arousal (Dove & Wiederman, 2000). Greater erotica consumption habits could lead to a better familiarity with the erotic stimuli used during the testing protocol, and this decreased novelty could produce more accurate responses for subjective sexual arousal. Using a "bottom-up" cognitive model in which people use physical sensations to infer emotional states, it is likely that increased sexual experience will lead to higher levels of concordance. Opposite-sex attracted participants (24 men and 25 women) will view a series of audiovisual stimuli depicting heterosexual sexual acts and neutral subjects. Participants will answer a series of questionnaires about their sexual history and attitudes, and will answer questions on their level of sexual arousal before and after each stimulus. Participants will continuously report their levels of subjective sexual arousal while simultaneously their genital responses, heart rate and skin conductance will be recorded. It is important to further our understanding of how much impact a participant's previous exposure to erotica, and masturbation behaviours to that erotica, have on their concordance rates; given the increasing pervasiveness and accessibility of erotica, this may prove extremely relevant to future  nvestigations

Concussion in sports in children and adolescents – to play or not to play?

2017 ◽  
Vol 65 (3) ◽  
Keyword(s):  
Gender Differences ◽  
Children And Adolescents ◽  
Return To Sport ◽  
Consensus Conference ◽  
Return To School ◽  
The Novel ◽  
International Consensus ◽  
Persistent Symptoms ◽  
New Findings

A lot has been published on the topic concussion in sports during the last years, conscience was sharpened, much was structured and defined more precisely, help tools were developed and rules changed. This article summarizes the fifth edition of the recently published guidelines of the “International Consensus Conference on Concussion in Sport”. In addition, new findings regarding gender differences and recovery will be presented, as well as the modified “return-to-sport” and the novel “return-to-school” protocols. Despite increased knowledge many questions remain such as the therapy of persistent symptoms or long-term sequelae of recurrent concussions.

Gender Differences in Empathy During Adolescence: Does Emotional Self-Awareness Matter?

2021 ◽  
pp. 003329412097663
Author(s):  
Cristina Trentini ◽  
Renata Tambelli ◽  
Silvia Maiorani ◽  
Marco Lauriola
Keyword(s):  
Gender Differences ◽  
Reactivity Index ◽  
Self Awareness ◽  
Emotional States ◽  
Personal Distress ◽  
Full Knowledge ◽  
Emotional Concern ◽  
Insight Into ◽  
Tas 20

Empathy refers to the capacity to experience emotions similar to those observed or imagined in another person, with the full knowledge that the other person is the source of these emotions. Awareness of one's own emotional states is a prerequisite for self-other differentiation to develop. This study investigated gender differences in empathy during adolescence and tested whether emotional self-awareness explained these differences. Two-hundred-eleven adolescents (108 girls and 103 boys) between 14 and 19 years completed the Interpersonal Reactivity Index (IRI) and the Toronto Alexithymia Scale (TAS-20) to assess empathy and emotional self-awareness, respectively. Overall, girls obtained higher scores than boys on IRI subscales like emotional concern, personal distress, and fantasy. Regarding emotional self-awareness, we found gender differences in TAS-20 scores, with girls reporting greater difficulty identifying feelings and less externally oriented thinking than boys. Difficulty identifying feelings explained the greatest personal distress experienced by girls. Lower externally oriented thinking accounted for girls’ greater emotional concern and fantasy. These findings offer an insight into the role of emotional self-awareness–which is essential for self-other differentiation–as an account for gender differences in empathic abilities during adolescence. In girls, difficulty identifying feelings can impair the ability to differentiate between ones’ and others’ emotions, leading them to experience self-focused and aversive responses when confronted with others’ suffering. Conversely, in boys, externally oriented thinking can mitigate personal distress when faced with others’ discomfort.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

scholarly journals Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1668
Author(s):  
Zongming Dai ◽  
Kai Hu ◽  
Jie Xie ◽  
Shengyu Shen ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Extraction Methods ◽  
Knowledge Network ◽  
Sensor Data ◽  
Support Vector ◽  
Bipartite Network ◽  
Classification Models ◽  
Text Data ◽  
Domain Experts ◽  
Problems And Solutions

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

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