1,720,974 research outputs found
A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems
No full textPsycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user’s cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ -band activity have been observed in high intense games and simulations
Connectivity Analysis Using Functional Brain Networks to Evaluate Cognitive Activity during 3D Modelling
No full textModelling 3D objects in CAD software requires special skills which require a novice user to undergo a series of training exercises to obtain. To minimize the training time for a novice user, the user-dependent factors must be studied. we have presented a comparative analysis of novice/expert information flow patterns. We have used Normalized Transfer Entropy (NTE) and Electroencephalogram (EEG) to investigate the differences. The experiment was divided into three cognitive states i.e., rest, drawing, and manipulation. We applied classification algorithms on NTE matrices and graph theory measures to see the effectiveness of NTE. The results revealed that the experts show approximately the same cognitive activation in drawing and manipulation states, whereas for novices the brain activation is more in manipulation state than drawing state. The hemisphere- and lobe-wise analysis showed that expert users have developed an ability to control the information flow in various brain regions. On the other hand, novice users have shown a continuous increase in information flow activity in almost all regions when doing drawing and manipulation tasks. A classification accuracy of more than 90% was achieved with a simple K-nearest neighbors (k-NN) to classify novice and expert users. The results showed that the proposed technique can be used to develop adaptive 3D modelling systems
Analysis of EEG signals & cognitive activity in 3D modeling for a multi modal interface system
No full textThe human brain uses a complex network of billions of neurons functioning together. Through learning and experience, the human brain establishes millions of connections between neurons. Although the individual functions of neurons are known, how these neurons work in a network to perform cognitive processes still requires research and investigation. Every human being has their own learning rate to understand things and develop a skill-set. There is a need for adaptive systems to change the pace of learning according to the user's competency level to have an impact on performance. This thesis explores the application of EEG signals to estimate the cognitive activity of competent and novice users in a design task. The main goal of this thesis is to identify the user's competency using cognitive activities acquired through EEG signals in an MMIS. We developed a multimodal interface system (MMIS) (xDe-SIGN v2) that allows the users to model a 3D object using speech and gesture modalities. We used Microsoft speech recognition API to detect and decode speech input and a Leap Motion sensor and API for gesture recognition. Research questions are classified into 6 groups: input modality, psychophysiological analysis, cognitive activity, information processing, and competency classification. The research questions are investigated in four major parts: a) the design and development of an MMIS (Chapter 4) b) qualitative evaluation of MMIS using speech and gestures for 3D modelling (Chapter4) c) quantitative evaluation of MMIS using EEG signals (Chapter 6-9) d)classification of user's competency level for adaptive systems design (Chapter10). We tested the usability of the system in 2 sets of experiments with 12 participants. We used EEG signals to record users' mental states and cognitive activity. First, we analyzed users' cognitive activity in a unimodal system(using keyboard and mouse inputs), and then, in a multimodal system (using speech and gesture inputs). We used a combination of qualitative methods such as questionnaires and quantitative methods such as EEG bands, Power Spectral Density (PSD) and Functional Brain Networks (FBN) to investigate the cognitive activity of novice and competent users. Our qualitative evaluation results supported by questionnaires indicate that speech and gestures were well-coordinated in human to human communication but not in human-computer interaction (HCI). However, speech and gestures could be used in HCI with proper pre-processing and optimization techniques, as 90% of the participants completed the given task with reasonable precision in xDe-SIGN v2. Our quantitative evaluation results supported by EEG power analysis showed that there are significant differences in the alpha, beta, and theta band activity of novice and competent users. The results also suggest that physical actions such as drawing, manipulation and moving 3D models have a direct impact on users' performance defined by task completion time, as competent users performed 1.5 times more physical actions than novices who had twice as many conceptual actions as competent users. These findings suggest that the structure of cognitive actions is the key to high performance. Directional FBN analysis also indicates significant differences in cognitive activity in both novice and competent users in various states. The cognitive activity is more intense while the participants use speech and gestures for 3D modelling. The frontal region of the brain is mostly active, which indicates the use of short-term memory. The thesis provides experimental evidence that EEG based measures can be used as a quantitative metric to analyze cognitive activity in HCI. Finally, we have proposed a method to classify user's competency levels using convolutional neural networks and EEG signals. We obtained a classification accuracy of more than 88%, which shows the effectiveness of the proposed method. Thus, we conclude that the proposed method has a clear potential for developing state-of-the-art adaptive systems that can adapt to users' competency levels
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Analyzing Novice and Expert User’s Cognitive Load in using a Multi-Modal Interface System
No full textExpertise Classification using Functional Brain Networks and Normalized Transfer Entropy of EEG in Design Applications
No full textAppropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
EEG Signal Analysis in 3D Modelling to Identify Correlations Between Task Completion in Design User’s Cognitive Activities
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