Feature Extraction and Classification of Biosignals - Emotion Valence Detection from EEG Signals

In thisworkavalencerecognitionsystembasedonelectroencephalogramsispresented.Theperformanceof the systemisevaluatedfortwosettings:singlesubjects(intra-subject)andbetweensubjects(inter-subject). The featureextractionisbasedonmeasuresofrelativeenergiescomputedinshorttimeintervalsandcertain frequencybands.Thefeatureextractionisperformedeitheronsignalsaveragedoveranensembleoftrialsor on single-trialresponsesignals.Thesubsequentclassificationstageisbasedonanensembleclassifier,i.e.a random forestoftreeclassifiers.Theclassificationisperformedconsideringtheensembleaverageresponsesof all subjects(inter-subject)orconsideringthesingle-trialresponsesofsinglesubjects(intra-subject).Applying a properimportancemeasureoftheclassifier,featureeliminationhasbeenusedtoidentifythemostrelevant features of the decision making.info:eu-repo/semantics/publishedVersio

Physiological signal-based emotion recognition from wearable devices

The interest in computers recognizing human emotions has been increasing recently. Many studies have been done about recognizing emotions from physical signals such as facial expressions or from written text with good results. However, recognizing emotions from physiological signals such as heart rate, from wearable devices without physical signals have been challenging. Some studies have given good, or at least promising results. The challenge for emotion recognition is to understand how human body actually reacts to different emotional triggers and to find a common factors among people. The aim of this study is to find out whether it is possible to accurately recognize human emotions and stress from physiological signals using supervised machine learning. Further, we consider the question what type of biosignals are most informative for making such predictions. The performance of Support Vector Machines and Random Forest classifiers are experimentally evaluated on the task of separating stress and no-stress signals from three different biosignals: ECG, PPG and EDA. The challenges with these biosginals from acquiring them to pre-processing the signals are addressed and their connection to emotional experience is discussed. In addition, the challenges and problems on experimental setups used in previous studies are addressed and especially the usability problems of the dataset. The models implemented in this thesis were not able to accurately classify emotions using supervised machine learning from the dataset used. The models did not perform remarkably better than just randomly choosing labels. PPG signal however performed slightly better than ECG or EDA for stress detection

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Late Fusion Approach for Multimodal Emotion Recognition Based on Convolutional and Graph Neural Networks

The current trends in automatic emotion recognition encompass the application of deep learning techniques, as, if applied to a multimodal approach, give the most promising results. The study presented in the paper follows this trend - the objective of the research is to propose a deep learning-based solution allowing to recognize emotions in circumplex model with performance metrics on a par with the ones achieved by competitive solutions. The observation channels used are physiological signals i.e. electrocardiography, electroencephalography and electroder- mal activity, while the applied technique is late fusion with Graph and Convolutional Neural Networks. The solution is validated for the AMIGOS dataset and the achieved results are com- parable to the baseline methods. While already satisfactory, the results still leave a place for further investigations

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior.Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors.Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach.Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Brain-Heart Connection for Psychological Health

With growing stress in our lives, there has been a major impact on our hearts and brain, leading to several mental and heart-related problems that sometimes are fatal and so severe that they leave a spot on us for the rest of our lives. To see the effect of the stress level on our brain and heart, we have designed a model to analyze the EEG (Electroencephalography) and the ECG (Electrocardiography) signals. Our study aimed to correlate two biosignals, EEG and ECG. We have focused on two important emotions, namely valence and arousal. We used the DREAMER dataset, which consists of both ECG and EEG signals. We evaluated nine different classifiers, including nearest neighbors, linear SVM, RBF SVM, Gaussian process, Decision tree, Random-forest, Neural net, AdaBoost, and Naive Bayes. We found AdaBoost had the best mean accuracy (97%) but with the longest processing time of around 5-10 milliseconds, whereas other classifiers had a mean runtime of around 1 millisecond. We discuss three things: preprocessing and feature extraction of the dataset, evaluation of classifiers for arousal and valence, and data visualization for correlation of arousal and valence values for all the extracted features

A Review on EEG Signals Based Emotion Recognition

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