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

Intelligent wristbands for the automatic detection of emotional states for the elderly

Over the last few years, research on computational intelligence is being conducted to detect emotional states of people. This paper proposes the use of intelligent wristbands for the automatic detection of emotional states to develop an application which allows to monitor older people in order to improve their quality of life. The paper describes the hardware design and the cognitive module that allows the recognition of the emotional states. The proposed wristband also integrates a camera that improves the emotion detection.- Programa Operacional Temático Factores de Competitividade(POCI-01-0145-). MINECO/FEDER TIN2015-65515-C4- 1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon. This work is supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the projects UID/CEC/00319/2013 and Post-Doc scholarship SFRH/BPD/102696/201

A low-cost cognitive assistant

In this paper, we present in depth the hardware components of a low-cost cognitive assistant. The aim is to detect the performance and the emotional state that elderly people present when performing exercises. Physical and cognitive exercises are a proven way of keeping elderly people active, healthy, and happy. Our goal is to bring to people that are at their homes (or in unsupervised places) an assistant that motivates them to perform exercises and, concurrently, monitor them, observing their physical and emotional responses. We focus on the hardware parts and the deep learning models so that they can be reproduced by others. The platform is being tested at an elderly people care facility, and validation is in process.This work was partly supported by the FCT (Fundação para a Ciência e Tecnología) through the Post-Doc scholarship SFRH/BPD/102696/2014 (A. Costa), by the Generalitat Valenciana (PROMETEO/2018/002), and by the Spanish Government (RTI2018-095390-B-C31)

Coverage of emotion recognition for common wearable biosensors

The present research proposes a novel emotion recognition framework for the computer prediction of human emotions using common wearable biosensors. Emotional perception promotes specific patterns of biological responses in the human body and this can be sensed and used to predict emotions using only biomedical measurements. Based on theoretical and empirical psychophysiological research, the foundation of autonomic specificity facilitates the establishment of a strong background for recognising human emotions using machine learning on physiological patterning. However, a systematic way of choosing the physiological data covering the elicited emotional responses for recognising the target emotions is not obvious. The current study demonstrates through experimental measurements the coverage of emotion recognition using common off-the-shelf wearable biosesnors based on the synchronisation between audiovisual stimuli and the corresponding physiological responses. The work forms the basis of validating the hypothesis for emotional state recognition in the literature, and presents coverage of the use of common wearable biosensors coupled with a novel preprocessing algorithm to demonstrate the practical prediction of the emotional states of wearers

Analyzing the impact of Driving tasks when detecting emotions through brain–computer interfaces

Traffic accidents are the leading cause of death among young people, a problem that today costs an enormous number of victims. Several technologies have been proposed to prevent accidents, being brain–computer interfaces (BCIs) one of the most promising. In this context, BCIs have been used to detect emotional states, concentration issues, or stressful situations, which could play a fundamental role in the road since they are directly related to the drivers’ decisions. However, there is no extensive literature applying BCIs to detect subjects’ emotions in driving scenarios. In such a context, there are some challenges to be solved, such as (i) the impact of performing a driving task on the emotion detection and (ii) which emotions are more detectable in driving scenarios. To improve these challenges, this work proposes a framework focused on detecting emotions using electroencephalography with machine learning and deep learning algorithms. In addition, a use case has been designed where two scenarios are presented. The first scenario consists in listening to sounds as the primary task to perform, while in the second scenario listening to sound becomes a secondary task, being the primary task using a driving simulator. In this way, it is intended to demonstrate whether BCIs are useful in this driving scenario. The results improve those existing in the literature, achieving 99% accuracy for the detection of two emotions (non-stimuli and angry), 93% for three emotions (non-stimuli, angry and neutral) and 75% for four emotions (non-stimuli, angry, neutral and joy)

Poetry in Pandemic: A Multimodal Neuroaesthetic Study on the Emotional Reaction to the Divina Commedia Poem

Poetry elicits emotions, and emotion is a fundamental component of human ontogeny. Although neuroaesthetics is a rapidly developing field of research, few studies focus on poetry, and none address its different modalities of fruition (MOF) of universal cultural heritage works, such as the Divina Commedia (DC) poem. Moreover, alexithymia (AX) resulted in being a psychological risk factor during the Covid-19 pandemic. The present study aims to investigate the emotional response to poetry excerpts from different cantica (Inferno, Purgatorio, Paradiso) of DC with the dual objective of assessing the impact of both the structure of the poem and MOF and that of the characteristics of the acting voice in experts and non-experts, also considering AX. Online emotion facial coding biosignal (BS) techniques, self-reported and psychometric measures were applied to 131 literary (LS) and scientific (SS) university students. BS results show that LS globally manifest more JOY than SS in both reading and listening MOF and more FEAR towards Inferno. Furthermore, LS and SS present different results regarding NEUTRAL emotion about acting voice. AX influences listening in NEUTRAL and SURPRISE expressions. DC’s structure affects DISGUST and SADNESS during listening, regardless of participant characteristics. PLEASANTNESS varies according to DC’s structure and the acting voice, as well as AROUSAL, which is also correlated with AX. Results are discussed in light of recent findings in affective neuroscience and neuroaesthetics, suggesting the critical role of poetry and listening in supporting human emotional processing

TOBE: Tangible Out-of-Body Experience

We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing the inner states of users using physiological signals such as heart rate or brain activity. Tobe can take the form of a tangible avatar displaying live physiological readings to reflect on ourselves and others. Such a toolkit could be used by researchers and designers to create a multitude of potential tangible applications, including (but not limited to) educational tools about Science Technologies Engineering and Mathematics (STEM) and cognitive science, medical applications or entertainment and social experiences with one or several users or Tobes involved. Through a co-design approach, we investigated how everyday people picture their physiology and we validated the acceptability of Tobe in a scientific museum. We also give a practical example where two users relax together, with insights on how Tobe helped them to synchronize their signals and share a moment

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective