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      Review of Studies on User Research Based on EEG and Eye Tracking

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      Applied Sciences
      MDPI AG

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          Abstract

          Under the development of interdisciplinary fusion, user research has been greatly influenced by technology-driven neuroscience and sensory science, in terms of thinking and methodology. The use of technical methods, such as EEG and eye-tracking, has gradually become a research trend and hotspot in this field, in order to explore the deep cognitive states behind users’ objective behaviors. This review outlines the applications of EEG and eye-tracking technology in the field of user research, with the aim of promoting future research and proposing reliable reference indicators and a research scope. It provides important reference information for other researchers in the field. The article summarizes the key reference indicators and research paradigms of EEG and eye-tracking in current user research, focusing on the user research situation in industrial products, digital interfaces and spatial environments. The limitations and research trends in current technological applications are also discussed. The feasibility of experimental equipment in outdoor environments, the long preparation time of EEG experimental equipment, and the accuracy error of physiological signal acquisition are currently existing problems. In the future, research on multi-sensory and behavioral interactions and universal studies of multiple technology fusions will be the next stage of research topics. The measurement of different user differentiation needs can be explored by integrating various physiological measurements such as EEG signals and eye-tracking signals, skin electrical signals, respiration, and heart rate.

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          How to get statistically significant effects in any ERP experiment (and why you shouldn't).

          ERP experiments generate massive datasets, often containing thousands of values for each participant, even after averaging. The richness of these datasets can be very useful in testing sophisticated hypotheses, but this richness also creates many opportunities to obtain effects that are statistically significant but do not reflect true differences among groups or conditions (bogus effects). The purpose of this paper is to demonstrate how common and seemingly innocuous methods for quantifying and analyzing ERP effects can lead to very high rates of significant but bogus effects, with the likelihood of obtaining at least one such bogus effect exceeding 50% in many experiments. We focus on two specific problems: using the grand-averaged data to select the time windows and electrode sites for quantifying component amplitudes and latencies, and using one or more multifactor statistical analyses. Reanalyses of prior data and simulations of typical experimental designs are used to show how these problems can greatly increase the likelihood of significant but bogus results. Several strategies are described for avoiding these problems and for increasing the likelihood that significant effects actually reflect true differences among groups or conditions.
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            An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior

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              EEG Source Imaging: A Practical Review of the Analysis Steps

              The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
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                Author and article information

                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                June 2023
                May 26 2023
                : 13
                : 11
                : 6502
                Article
                10.3390/app13116502
                49c8283a-1bd5-4ee6-ac26-345a98105047
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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