Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy

A survey of comics research in computer science

Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, human-computer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks

Cognitive Load Measurement Based on EEG Signals

Measurement of cognitive load should be advantageous in designing an intelligent navigation system for the visually impaired people (VIPs) when navigating unfamiliar indoor environments. Electroencephalogram (EEG) can offer neurophysiological indicators of perceptive process indicated by changes in brain rhythmic activity. To support the cognitive load measurement by means of EEG signals, the complexity of the tasks of the VIPs during navigating unfamiliar indoor environments is quantified considering diverse factors of well-established signal processing and machine learning methods. This chapter describes the measurement of cognitive load based on EEG signals analysis with its existing literatures, background, scopes, features, and machine learning techniques

The multimodal parameter enhancement of electroencephalogram signal for music application

Blinding of modality has been influenced decision of multimodal in several circumstances. Sometimes, certain electroencephalogram (EEG) signal is omitted to achieve the highest accuracy of performance. Therefore, the aim for this paper is to enhance the multimodal parameters of EEG signals based on music applications. The structure of multimodal is evaluated with performance measure to ensure the implementation of parameter value is valid to apply in the multimodal equation. The modalities’ parameters proposed in this multimodal are weighted stress condition, signal features extraction, and music class. The weighted stress condition was obtained from stress classes. The EEG signal produces signal features extracted from the frequency domain and time-frequency domain via techniques such as power spectrum density (PSD), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). Power value is evaluated in PSD. The energy distribution is derived from STFT and CWT techniques. Two types of music were used in this experiment. The multimodal fusion is tested using a six-performance measurement method. The purposed multimodal parameter shows the highest accuracy is 97.68%. The sensitivity of this study presents over 95% and the high value for specificity is 89.5%. The area under the curve (AUC) value is 1 and the F1 score is 0.986. The informedness values range from 0.793 to 0.812 found in this paper

Hybrid system of emotion evaluation in physiotherapeutic procedures

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%)

Envisioning Transitions. Bodies, buildings, and boundaries

“Transition” is the dynamic process of changing state, going beyond, crossing over, and passing from one point to the next. The signification of the word is close to that of evolution, modification, mutation, and transformation, all of which are confined into a strictly restricted timeframe. Etymologically, “transitions” can be nothing else than temporary: they appear silently, burst, violently establish, and gradually disappear into reality. In their blinding momentariness, “transitions” bear with them the positive undertone of change and renewal, along with the hopefulness of that which is unknown.  If the term “transition” recurs regularly in the contemporary vocabulary of architecture and design cultures, this repetition reveals a period characterized by overlapping and sequential changes. The word is without a doubt overused, but not without reason. Indeed, we find ourselves in an unusually extended period of consecutive “transitions”, overwhelmingly undefined in temporality and ambitions. As we are witnessing societies go through stark demographic, political, economic, and cultural changes, the intersecting problematics (e.g., ecological, digital, pandemic, etc.) form a rather complex topography of change, negatively charged by the instability of dilated time and the uncertainty of undefined destination. The word is employed with the confidence of a natural process, as if it were a storm, and while we affirm our existence in “transition”, we nod our troubled times away. Whether positively or negatively perceived, “transitions” form bridges between histories. Yet, what does it actually mean to be in “transition”? Can we define it as an autonomous and productive period whose importance could go beyond a starting and an ending date? How are “transitions” impacting and being impacted by human spaces, the built environment, and design cultures? What are some concrete, practical case studies that demonstrate how “transitions” could affect architecture and design cultures while emphasizing the role that these disciplines play in transitional processes? It is within this backdrop that we put forward the theme of “transition”—in all its simplicity and complexity

Developing Driving Behaviour Models Incorporating the Effects of Stress

Driving is a complex task and several factors influence drivers’ decisions and performance including traffic conditions, attributes of vehicles, network and environmental characteristics, and last but not least characteristics of the drivers themselves. in an effort to better explain and represent driving behaviour, several driving behaviour models have been suggested over the years. In the existing literature, there are two main streams of driving behaviour models that can be found. The first is approaching driving behaviour from a human factors and cognitive perspective while the second is engineering-based. Driving behaviour models of the latter category are mathematical representations of drivers’ behaviour at the individual level, mostly focussing on acceleration/deceleration, lane-change and gap-acceptance decisions. Many of these factors are captured by existing driving behaviour models used in microscopic simulation tools. However, while the vast majority of existing models is approximating driving behaviour, primarily focusing on the effects of traffic conditions, little attention has been given to the impact of drivers’ characteristics. The aim of the current thesis is to investigate the effects of stress on driving behaviour and quantify its impact using an econometric modelling framework. This main research question emerged as a result of a widely acknowledged research gap in existing engineering-based driving behaviour models related to the incorporation of human factors and drivers’ characteristics within the model specification. The research was based on data collected using the University of Leeds Driving Simulator. Two main scenarios were presented to participants, while they were also deliberately subjected to stress induced by time pressure and various scenarios. At the same time, stress levels were measured via physiological indicators. Sociodemographic and trait data was also collected in the form of surveys. The data has been initially analysed for each main scenario and several statistics are extracted. The results show a clear effect of time pressure in favour of speeding, however relations related to physiological responses are not always clear. Moreover, two driving behaviour models are developed, a gap-acceptance and a car-following model. In the former model, increase in physiological responses is related to higher probability of accepting a gap and time pressure has a positive effect of gap-acceptance probability as well. In the car-following model, stress is associated with increased acceleration and potentially a more aggressive driving style. The aforementioned analysis is based on data collected in a driving simulator. Given the potential differences in driving behaviour between real and simulated driving, the transferability of a model based on the latter data to field traffic setting is also investigated. Results indicate significant differences in parameters estimated from a video and the simulator dataset, however these differences can be significantly reduced after applying parameter updating techniques. The findings in this thesis show that stress and drivers’ characteristics can influence driving behaviour and thus should be considered in the driving behaviour models for microscopic simulation applications. However, for real life applications, it is suggested that the extent of these effects should be treated with caution and ideally rescaled based on real traffic observations