A Review of Emotion Recognition Using EEG Data and Machine Learning Techniques

Using AI to help humans with handling their emotions and identifying their stress levels in the current stressful lifestyle will greatly help them manage their lifestyle. Using the deep learning techniques, it can be made possible by creating a virtual bot to observe and understand human emotions.   In this paper, the researcher try to review the comments from Reddit that are used, preprocessed and trained using Deep Neural Network to learn the emotions of the user. The inference engine module, which is a hybrid network consisting of convolutional neural network and recurrent neural network, is also interfaced. The model provides a high accuracy of response. The selection of frequency bands plays an important role in discerning patterns of brain-related emotions. This document explores a new method for selecting appropriate thematic bands instead of using fixed bands to detect emotions. A common spatial technique and machine   machines were used to classify the emotional states.  This document describes a number of possible technologies aimed at communication and other applications; however, they represent only a small sample of the extensive future potential of these technologies.  We have also focused on relatively anticipated breakthroughs in the discussion of applications in sensory, BCI technologies; but breakthroughs like the new portable sensor technology, which offers ultra-high-resolution spatial and time-based activity in the brain, opens the door to a much broader range of applications. Keywords: Emotions, EEG, Machine Learning, Deep Learning, Systems and Signals DOI: 10.7176/ISDE/11-4-04 Publication date:August 31st 2020

Anxiety reducing through a neurofeedback serious game with dynamic difficulty adjustment

Presently, society has to deal with a large number of mental issues. Anxiety disorder is a serious concern, affecting millions of people’s lives and, although methods to tackle the problem currently exist, these main treatments are being linked to some issues and improvements must be found. One of the alternatives is Neurofeedback, a biofeedback treatment, completely non-invasive and showing impressive results so far. It uses a neuroheadset equipment to read the neural activity of the brain, giving the user visual feedback about it. The purpose this, is to train the users’ brain in specific regions and frequencies, allowing the subjects to learn how to voluntarily control its neural activity, even outside of the session. Current applications using this method might be too simple, which can become tedious and disengaging. Serious games can help with these issues, since it can bring enjoyment and engagement while doing this type of treatment. The interest in games’ capabilities in education has been increasing over the past years, since it has been proved that games are an excellent tool for education and skill learning. Joining these concepts of game and neurofeedback, this project aims to create a serious game prototype, applying the current treatment knowledge. The development process of a new game with neuroheadset integration, capable of reading the neural activity of the user while playing and giving the appropriate feedback, will be described in the present document. Since studies proved that a good balance between challenge and skill increases the learning performance, a dynamic difficulty adjustment system is implemented within the game, allowing the game to adapt itself to each user’s skill individually, and keeping the user in a challenging, motivating zone. At the end of the document, the results of pilot test on a few subjects are shown.Na sociedade actual o número de problemas relacionados com perturbações mentais tem sido cada vez mais relevante, sendo esse o caso da ansiedade. O distúrbio de ansiedade é um problema que atinge milhões de pessoas e, embora existam métodos para combater este problema, estudos comprovam que estes têm algumas lacunas que podem trazer outros problemas associados, sendo portanto necessário procurar melhorias aos métodos actuais. Uma das alternativas tem apresentado excelentes resultados e denomina-se Neurofeedback. Este é um tratamento de biofeedback, nãoinvasivo e que utiliza um equipamento neuroheadset para capturar a actividade neuronal, apresentando indicações visuais sobre o comportamento do utilizador. Isto é feito com o objectivo de treinar o cérebro do utilizador, em regiões e frequências específicas, para que este seja capaz de controlar voluntariamente a sua actividade neuronal. As aplicações actualmente utilizadas com este intuito podem se tornar aborrecidas e monótonas devido à sua simplicidade. Um jogo sério pode ajudar com estes problemas, uma vez que é capaz de trazer divertimento e motivação para este tipo de tratamento. O crescente interesse nas capacidades educativas dos jogos sérios, tem identificado estes como excelentes ferramentas para a educação. Este projecto pretende portanto criar um protótipo de um jogo sério, aplicando os conceitos de neurofeedback. Neste documento, é apresentado o processo de desenvolvimento de um novo jogo com integração de um neuroheadset, capaz de identificar a actividade neuronal do jogador dando respostas adequadas. Uma vez que estudos comprovam que um bom balanço entre desafio apresentado e técnica do utilizador aumenta a capacidade de aprendizagem, foi implementado também um sistema de ajuste de dificuldade dinâmica, permitindo uma adaptação do jogo a cada indivíduo e mantendo este numa zona motivante de equilíbrio entre desafio e proficiência. No final serão apresentados os resultados de um teste piloto efectuado em alguns indivíduos

Framework of controlling 3d virtual human emotional walking using BCI

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique. © 2015 Penerbit UTM Press. All rights reserved

A Survey of Brain Computer Interface Using Non-Invasive Methods

Research on Brain-Computer Interface (BCI) began in the 1970s and has increased in volume and diversified significantly since then. Today BCI is widely used for applications like assistive devices for physically challenged users, mental state monitoring, input devices for hands-free applications, marketing, education, security, games and entertainment. This article explores the advantages and disadvantages of invasive and non-invasive BCI technologies and focuses on use cases of several non-invasive technologies, namely electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI), Near Infrared Spectroscopy (NIRs) and hybrid systems

Variation of linear and nonlinear parameters in the swim strokes according to the level of expertise

The aim was to examine the variation of linear and nonlinear proprieties of the behavior in participants with different levels of swimming expertise among the four swim strokes. Seventy-five swimmers were split into three groups (highly qualified experts, experts and nonexperts) and performed a maximal 25m trial for each of the four competitive swim strokes. A speed-meter cable was attached to the swimmer's hip to measure hip speed; from which speed fluctuation (dv), approximate entropy (ApEn) and fractal dimension (D) variables were derived. Although simple main effects of expertise and swim stroke were obtained for dv and D, no significant interaction of expertise and stroke were found except in ApEn. The ApEn and D were prone to decrease with increasing expertise. As a conclusion, swimming does exhibit nonlinear properties but its magnitude differs according to the swim stroke and level of expertise of the performer.info:eu-repo/semantics/publishedVersio

Investigating the effects of neuromodulatory training on autistic traits: a multi-methods psychophysiological study.

Autism spectrum disorder (ASD) is characterized by noticeable difficulties with social interaction and communication. Building on past research in this area and with the aim of improving methodological perspectives, a multi method approach to the study of ASD, mirror neurons and neurofeedback was taken. This thesis is made up of three main experiments: 1) A descriptive study of the resting state electroencephalography (EEG) across the spectrum of autistic traits in neurotypical individuals, 2) A comparison of 3 EEG protocols on MNs activation (mu suppression) and its difference according to self-reported traits of autism in neurotypical individuals, and 3) Neurofeedback training (NFT) on individuals with high autistic traits. In chapters 3 and 4 we employed simultaneous monitoring of physiological data. For chapter 3 EEG and eye-tracking was used, In the case of chapter 4, EEG and eye-tracking as well functional near infrared spectroscopy (fNIRS). Overall the findings revealed differences in mu rhythm reactivity associated to AQ traits. In chapter 2, the rEEG showed that individuals with high AQ scores showed less activation of frontal and fronto-central regions combined with higher levels of complexity in fronto-temporal, temporal, parietal and parieto-occipital areas. In chapter 3, EEG protocols that elicited Mu reactivity in individuals with different AQ traits suggested that as the AQ traits become more pronounced in neurotypical population, the event-related desynchronization (ERD) in low alpha declines. Chapter 3 was also the basis for the choice of pre/post assessment for chapter 4. In chapter 4 the multi-method physiological approach provided parallel physiological evidence for the effects of NFT in sensorimotor reactivity, namely, an increase in ERD in high alpha, higher levels of oxygenated haemoglobin and changes to the amplitude and frequency in the microstructure of mu for participants who underwent active training as opposed to a sham group

Electroencephalographic Measures of Depressivity: Alpha Asymmetry and Fractal Dimension

Recent research has suggested that neurofeedback, utilising alpha asymmetry or fractal dimension as an index of depression, may be an effective treatment for depressed individuals. In this thesis the relationships between frontal alpha asymmetry (FAA), parietal alpha asymmetry (PAA) and Higuchi’s fractal dimension (HFD) with PID-5 depressivity were investigated to assess their potential as signals for neurofeedback. Resting EEG previously recorded from a general sample of 66 individuals was analysed. The data of male and female participants was analysed separately. Optimised eye condition and bandwidth were determined with one way, repeated measure ANOVAs. Optimal specific measures of FAA, PAA and HFD were then identified by the proportion of PID-5 depressivity accounted for. The optimal FAA measure was obtained from the frontopolar electrode pair (Fp2 – Fp1) in the 10-12Hz sub-band. It was the only measure that was reliable in both male and female participants. PAA between the lateral electrode pair (P8 – P7) in the 8-10hz sub-band reliably correlated with depressivity in female, but not male, participants. HFD was reliable at every electrode in female, but not male, participants and displayed intercorrelation between all electrodes in both genders. A combined model using all three optimal measures showed that the proportional variance of FAA, PAA and HFD was mainly additive, with little variance shared between measures. The results suggest that FAA at the frontopolar electrode pair (Fp2 – Fp1) in the 10 – 12Hz band may be the optimal AA measure for neurofeedback protocols targeting depression. Correlations between AA and depressivity were often site- and band-specific; reliable correlations observed in one location did not necessarily generalise to other locations. Correlations between HFD and depressivity were not site-specific with most variance shared between sites. There was little overlap between the variance accounted for by FAA, PAA and HFD; indicating that the information conveyed by each is due to distinct neural processes, which may be associated with distinct aspects of depressivity and, potentially, other trait measures. Future work should assess replicability and the extent to which the results are specific to depressivity

Comparison of classical kinematics, entropy, and fractal properties as measures of complexity of the motor system in swimming

The aim of this study was to compare the non-linear properties of the four competitive swim strokes. Sixty-eight swimmers performed a set of maximal 4 × 25 m using the four competitive swim strokes. The hip's speed-data as a function of time was collected with a speedo-meter. The speed fluctuation (dv), approximate entropy (ApEn) and the fractal dimension by Higuchi's method (D) were computed. Swimming data exhibited non-linear properties that were different among the four strokes (14.048 ≤ dv ≤ 39.722; 0.682 ≤ ApEn ≤ 1.025; 1.823 ≤ D ≤ 1.919). The ApEn showed the lowest value for front-crawl, followed by breaststroke, butterfly, and backstroke (P < 0.001). Fractal dimension and dv had the lowest values for front-crawl and backstroke, followed by butterfly and breaststroke (P < 0.001). It can be concluded that swimming data exhibits non-linear properties, which are different among the four competitive swimming strokes

Thought-controlled games with brain-computer interfaces

Nowadays, EEG based BCI systems are starting to gain ground in games for health research. With reduced costs and promising an innovative and exciting new interaction paradigm, attracted developers and researchers to use them on video games for serious applications. However, with researchers focusing mostly on the signal processing part, the interaction aspect of the BCIs has been neglected. A gap between classification performance and online control quality for BCI based systems has been created by this research disparity, resulting in suboptimal interactions that lead to user fatigue and loss of motivation over time. Motor-Imagery (MI) based BCIs interaction paradigms can provide an alternative way to overcome motor-related disabilities, and is being deployed in the health environment to promote the functional and structural plasticity of the brain. A BCI system in a neurorehabilitation environment, should not only have a high classification performance, but should also provoke a high level of engagement and sense of control to the user, for it to be advantageous. It should also maximize the level of control on user’s actions, while not requiring them to be subject to long training periods on each specific BCI system. This thesis has two main contributions, the Adaptive Performance Engine, a system we developed that can provide up to 20% improvement to user specific performance, and NeuRow, an immersive Virtual Reality environment for motor neurorehabilitation that consists of a closed neurofeedback interaction loop based on MI and multimodal feedback while using a state-of-the-art Head Mounted Display.Hoje em dia, os sistemas BCI baseados em EEG estão a começar a ganhar terreno em jogos relacionados com a saúde. Com custos reduzidos e prometendo um novo e inovador paradigma de interação, atraiu programadores e investigadores para usá-los em vídeo jogos para aplicações sérias. No entanto, com os investigadores focados principalmente na parte do processamento de sinal, o aspeto de interação dos BCI foi negligenciado. Um fosso entre o desempenho da classificação e a qualidade do controle on-line para sistemas baseados em BCI foi criado por esta disparidade de pesquisa, resultando em interações subótimas que levam à fadiga do usuário e à perda de motivação ao longo do tempo. Os paradigmas de interação BCI baseados em imagética motora (IM) podem fornecer uma maneira alternativa de superar incapacidades motoras, e estão sendo implementados no sector da saúde para promover plasticidade cerebral funcional e estrutural. Um sistema BCI usado num ambiente de neuro-reabilitação, para que seja vantajoso, não só deve ter um alto desempenho de classificação, mas também deve promover um elevado nível de envolvimento e sensação de controlo ao utilizador. Também deve maximizar o nível de controlo nas ações do utilizador, sem exigir que sejam submetidos a longos períodos de treino em cada sistema BCI específico. Esta tese tem duas contribuições principais, o Adaptive Performance Engine, um sistema que desenvolvemos e que pode fornecer até 20% de melhoria para o desempenho específico do usuário, e NeuRow, um ambiente imersivo de Realidade Virtual para neuro-reabilitação motora, que consiste num circuito fechado de interação de neuro-feedback baseado em IM e feedback multimodal e usando um Head Mounted Display de última geração