Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals

The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub

Group emotion modelling and the use of middleware for virtual crowds in video-games

In this paper we discuss the use of crowd simulation in video-games to augment their realism. Using previous works on emotion modelling and virtual crowds we define a game world in an urban context. To achieve that, we explore a biologically inspired human emotion model, investigate the formation of groups in crowds, and examine the use of physics middleware for crowds. Furthermore, we assess the realism and computational performance of the proposed approach. Our system runs at interactive frame-rate and can generate large crowds which demonstrate complex behaviour

Water as a matter for human emotions

The purpose of this thesis is to explore the plastic image of water as the metaphor for specific human emotions. Given its fluid nature, water itself does not have its own particular form of existence. However, influenced by the external environment, water creates various forms that suggest specific human emotion. These forms are created by forces of nature. I intend to create sculpture of the motion of water that evokes human emotion. The format for the idea is sculpture that consists of structural, repetitive and organized modules. I exploit the repetitive and sequential use of forms that provide an immediate feeling of visual unity. In this thesis I will discuss how my sculptural work explores water as a metaphor for human emotion

Applications of Kort Spiral Learning Method on Learners Behaviour Based on Wavelet Transform Method(DWT) in E-Learning Environment

This paper is planning to address one of the important difficulties faced by the e-learning communities, that is, capturing of human emotion accurately both of a tutor and learner in e-learning sceanario. In this paper, an approach for human emotion recognition system based on Discrete Wavelet Transform (DWT) on korts spiral model of learning on learners and tutors is presented. The affective pedagogy is one of the important component in effective learning model. The Korts model helps us to understand the effectiveness of learners emotion in the learning environment. The Korts model can be better implemented by means of human emotion recognition system based on DWT method. The classification of human emotional state is achieved by extracting the energies from all sub-bands of DWT. The robust K-Nearest Neighbor (K-NN) is constructed for classification. The evaluation of the system is carried on using JApanese Female Facial Expression (JAFFE) database. Experimental results show that the proposed DWT based human emotion recognition system produces more accurate recognition rate which applied on Korts learning model we can able to produce the optimal e-learning environment(OELE)

Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM

A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normalized data, however, directly using of these features will lead to a low recognition rate. In order to gain the optimized features, a covariance based feature selection is employed in our method. Finally, a SVM with input of the optimized features is utilized to achieve the human emotion recognition. The experimental results indicate that the proposed method leads to good human emotion recognition, and the recognition accuracy is more than 66.67%

The Design of Computer Interfaces Adaptive to Human Emotion: Current Issues and Research Directions

Despite the rapid advancement of computer technology, computers remain incapable of understanding human emotion. As a result, users have often been unaided for their aversive emotion that may take place during their computer tasks. This may be detrimental to positive and productive interactions between users and computers. This paper reviews some empirical studies regarding the effect of emotion on computer work and conceptualizes what constitutes an emotional computer. It is proposed that the emotional computer be designed to understand human emotion and adapt its interface accordingly. This paper raises a number of research questions in relation to such issues as measurement (e.g., automatic detection of human emotion, time delay), signal processing (e.g., accuracy) and user interfaces (e.g., ways to alleviate the intensity of negative emotion). Considering that there has been very little research on the design and aftermath of emotional computers, further studies are urgently needed

A Computational Vision on Human Emotion

Late 20th century Artificial Intelligence research treated emotion and cognition as antithetical entities. Recent neurological studies, however, suggest that the two are closely related. Emotion plays a critical role in decision making. Studies also have established that neurological deficits in emotion processing lead to deficiency in decision making. These findings have invoked a new interest in the modeling of emotion in artificially intelligent systems. The Dependable Computing and Networking Lab (DCNL) at ISU, led by Dr. Arun Somani, is researching human emotion modeling using Computer Vision. The study will engender novel ideas to adapt the existing emotion-modeling framework in the research realm to the needs of the Human and Object Detection project in the DCNL group. We believe that this study could also lead to new and innovative models of human emotion. Computational tools such as OpenCV and MATLAB will be used to test and validate new models and adaptations. Using machine learning methods, the reliability and efficacy of the new methods will also be evaluated

Human Emotion Recognition using Electrocardiogram Signals

Human emotions are recognized using face recognition, speech recognition, physiological signals recognition etc. This paper represents Electrocardiogram (ECG) signal for emotion recognition thorough analysis of its psychological properties to recognize human emotion, it can reflect peoples true emotion and provide smooth interface between human and computer. Each signal is empirically decomposed by using Empirical Mode Decomposition (EMD) into finite set of small oscillatory activity called Intrinsic Mode Functions (IMF). The information components of interest are then combined to create feature v ector based on the combination methods for exploiting the fission - fusion processes provided by Hilbert - Huang transform. In the next stage, classification is performed by using Multi class Support Vector Machines to identify four emotional states (joy, ange r, sadness and pleasure) of human body. When we evaluated the algorithm on database recorded at university of Augsburg, the proposed method achieved improved recognition accuracy for subj ect - independent classification

Facial Emotion Recognition Using Machine Learning

Face detection has been around for ages. Taking a step forward, human emotion displayed by face and felt by brain, captured in either video, electric signal (EEG) or image form can be approximated. Human emotion detection is the need of the hour so that modern artificial intelligent systems can emulate and gauge reactions from face. This can be helpful to make informed decisions be it regarding identification of intent, promotion of offers or security related threats. Recognizing emotions from images or video is a trivial task for human eye, but proves to be very challenging for machines and requires many image processing techniques for feature extraction. Several machine learning algorithms are suitable for this job. Any detection or recognition by machine learning requires training algorithm and then testing them on a suitable dataset. This paper explores a couple of machine learning algorithms as well as feature extraction techniques which would help us in accurate identification of the human emotion