Panteli, A., Kalaitzi, E., & Fidas, C. A. (2024) A Review on the Use of EEG for the Investigation of the Factors that Affect Consumer’s Behavior. Physiology & Behavior, 114509.
[March 2024]
Abstract:
This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer’s behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals’ acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers’ choices, but also those that studied consumers’ decisions as a result of the interactions that take place among the received marketing messages and the individual’s internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer’s behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied.
Thomopoulos, G. A., Lyras, D. P., & Fidas, C. A. (2024) A systematic review and research challenges on phishing cyberattacks from an electroencephalography and gaze-based perspective. Personal and Ubiquitous Computing, 1-22
[March 2024]
Abstract:
Phishing is one of the most important security threats in modern information systems causing different levels of damages to end-users and service providers such as financial and reputational losses. State-of-the-art anti-phishing research is highly fragmented and monolithic and does not address the problem from a pervasive computing perspective. In this survey, we aim to contribute to the existing literature by providing a systematic review of existing experimental phishing research that employs EEG and eye-tracking methods within multi-modal and multi-sensory interaction environments. The main research objective of this review is to examine articles that contain results of at least one EEG-based and/or eye-tracking-based experimental setup within a phishing context. The database search with specific search criteria yielded 651 articles from which, after the identification and the screening process, 42 articles were examined as per the execution of experiments using EEG or eye-tracking technologies in the context of phishing, resulting to a total of 18 distinct papers that were included in the analysis. This survey is approaching the subject across the following pillars: a) the experimental design practices with an emphasis on the applied EEG and eye-tracking acquisition protocols, b) the artificial intelligence and signal preprocessing techniques that were applied in those experiments, and finally, c) the phishing attack types examined. We also provide a roadmap for future research in the field by suggesting ideas on how to combine state-of-the-art gaze-based mechanisms with EEG technologies for advancing phishing research. This leads to a discussion on the best practices for designing EEG and gaze-based frameworks.
Kyriaki, K., Koukopoulos, D., & Fidas, C. A. (2024) A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment. IEEE Access.
[January 2024]
Abstract:
Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment. Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users’ cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.
Fidas, C. A., & Lyras, D. (2023). A Review of EEG-Based User Authentication: Trends and Future Research Directions. IEEE Access.
[March 2023]
Abstract:
Recently, the use of Electroencephalography (EEG) in scientific research on User Authentication (UA) has led to cutting-edge experiments that seek to identify and authenticate individuals based on their brain activity in particular usage scenarios. Utilizing EEG signals, derived from brain activity, might provide innovative solutions to contemporary security issues in traditional knowledge-based user authentication, including the threat of shoulder surfing. In this review paper, we analyze 108 different EEG-based user authentication experiments based on the following perspectives: a) the user experimental setup, with an emphasis on the applied EEG- protocols; b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad time frame from 1998 to 2022 and include various experimental protocols and algorithms used for classifying EEG signals. Additionally, the majority of the referenced works report findings from multiple experiments that incorporate distinct approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.
Papers
Papadoulis, G., Sintoris, C., Fidas, C., & Avouris, N. (2023, August). Extending User Interaction with Mixed Reality Through a Smartphone-Based Controller. In IFIP Conference on Human-Computer Interaction (pp. 426-435). Cham: Springer Nature Switzerland.
[August 2023]
Abstract:
A major concern in mixed-reality (MR) environments is to support intuitive and precise user interaction. Various modalities have been proposed and used, including gesture, gaze, voice, hand-recognition or even special devices, i.e. external controllers. However, these modalities may often feel unfamiliar and physically demanding to the end-user, leading to difficulties and fatigue. One possible solution worth investigating further is to use an everyday object, like a smartphone, as an external device for interacting with MR. In this paper, we present the design of a framework for developing an external smartphone controller to extend user input in MR applications, which we further utilize to implement a new interaction modality, a tap on the phone. We also report on findings of a user study (n=24) in which we examine performance and user experience of the suggested input modality through a comparative user evaluation task. The findings suggest that incorporating a smartphone as an external controller shows potential for enhancing user interaction in MR tasks requiring high precision, as well as pinpointing the value of providing alternative means of user input in MR applications depending on a given task and personalization aspects of an end-user.
Trigka, M., Papadoulis, G., Dritsas, E., & Fidas, C. (2023, August). Influences of Cognitive Styles on EEG-Based Activity: An Empirical Study on Visual Content Comprehension. In IFIP Conference on Human-Computer Interaction (pp. 496-500). Cham: Springer Nature Switzerland.
[August 2023]
Abstract:
This paper presents an empirical study that examines how human cognitive style affects brain signal activity when individuals engage in a visual content comprehension task. To facilitate this study, we adopted an accredited cognitive style framework (Field Dependent-Field Independent or FD-FI) and utilized a validated cognitive style elicitation task, namely the Group Embedded Figures Test (GEFT), to elicit visual content comprehension via static figures. Brain signal activity was captured through a high-precision EEG device and subsequently correlated with the GEFT-derived cognitive style. Furthermore, power spectral analysis allowed the observation of potential differences between the two cognitive style groups. Analysis of results yields different effects on FD and FI users and especially in the average power of brain signals in the cortical area. Identifying such brain signal variations between FD-FI users might lay the ground for designing novel real-time elicitation frameworks of human cognitive styles, thus providing innovative personalization and adaptation approaches in a variety of application domains.
Trigka, Maria, Elias Dritsas, and Christos Fidas (2022, November) A Survey on Signal Processing Methods for EEG-based Brain Computer Interface Systems
In Proceedings of the 26th Pan-Hellenic Conference on Informatics (pp. 213-218)
[March 2023]
Abstract:
The development of human-computer interaction (HCI) systems that will efficiently capture the human brain, the so-called Brain-Computer Interaction (BCI) systems, will bring a new era in various disciplines (gaming, education, cultural heritage, etc). Actually, it is expected that the design and development of an electroencephalography (EEG) based-driven framework for intelligent real-time modelling of human cognitive abilities will provide groundbreaking technological advances in the delivery of human cognition-centred personalized systems and significantly advance the state-of-the-art research in human brain modelling. The aim of this paper is to make a concise and focused presentation of Signal Processing and Artificial Intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL), and how these fields may help to model and thus predict human behaviour, emotion, cognitive state in different tasks.
Thesis
The Department of Electrical Engineering and Computer Technology utilizes the CogniX Infrastructure for conducting master's theses
Study of a PGA-EEG Authentication System Based on User Experiences Ioannis Katoikos [October 2023]
Design and development of an AI user authentication system leveraging on EEG data Foivos Chalantzoukas [October 2023]
Design and development of a user authentication system using electroencephalography (EEG) signals based on picture gesture authentication (PGA) environments Dimitrios Verginis [February 2024]
Journals
Panteli, A., Kalaitzi, E., & Fidas, C. A. (2024) A Review on the Use of EEG for the Investigation of the Factors that Affect Consumer’s Behavior. Physiology & Behavior, 114509.
Abstract:
This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer’s behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals’ acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers’ choices, but also those that studied consumers’ decisions as a result of the interactions that take place among the received marketing messages and the individual’s internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer’s behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied.
Thomopoulos, G. A., Lyras, D. P., & Fidas, C. A. (2024) A systematic review and research challenges on phishing cyberattacks from an electroencephalography and gaze-based perspective. Personal and Ubiquitous Computing, 1-22
Abstract:
Phishing is one of the most important security threats in modern information systems causing different levels of damages to end-users and service providers such as financial and reputational losses. State-of-the-art anti-phishing research is highly fragmented and monolithic and does not address the problem from a pervasive computing perspective. In this survey, we aim to contribute to the existing literature by providing a systematic review of existing experimental phishing research that employs EEG and eye-tracking methods within multi-modal and multi-sensory interaction environments. The main research objective of this review is to examine articles that contain results of at least one EEG-based and/or eye-tracking-based experimental setup within a phishing context. The database search with specific search criteria yielded 651 articles from which, after the identification and the screening process, 42 articles were examined as per the execution of experiments using EEG or eye-tracking technologies in the context of phishing, resulting to a total of 18 distinct papers that were included in the analysis. This survey is approaching the subject across the following pillars: a) the experimental design practices with an emphasis on the applied EEG and eye-tracking acquisition protocols, b) the artificial intelligence and signal preprocessing techniques that were applied in those experiments, and finally, c) the phishing attack types examined. We also provide a roadmap for future research in the field by suggesting ideas on how to combine state-of-the-art gaze-based mechanisms with EEG technologies for advancing phishing research. This leads to a discussion on the best practices for designing EEG and gaze-based frameworks.
Kyriaki, K., Koukopoulos, D., & Fidas, C. A. (2024) A Comprehensive Survey of EEG Preprocessing Methods for Cognitive Load Assessment. IEEE Access.
Abstract:
Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment. Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users’ cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.
Fidas, C. A., & Lyras, D. (2023). A Review of EEG-Based User Authentication: Trends and Future Research Directions. IEEE Access.
Abstract:
Recently, the use of Electroencephalography (EEG) in scientific research on User Authentication (UA) has led to cutting-edge experiments that seek to identify and authenticate individuals based on their brain activity in particular usage scenarios. Utilizing EEG signals, derived from brain activity, might provide innovative solutions to contemporary security issues in traditional knowledge-based user authentication, including the threat of shoulder surfing. In this review paper, we analyze 108 different EEG-based user authentication experiments based on the following perspectives: a) the user experimental setup, with an emphasis on the applied EEG- protocols; b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad time frame from 1998 to 2022 and include various experimental protocols and algorithms used for classifying EEG signals. Additionally, the majority of the referenced works report findings from multiple experiments that incorporate distinct approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.
Papers
Papadoulis, G., Sintoris, C., Fidas, C., & Avouris, N. (2023, August). Extending User Interaction with Mixed Reality Through a Smartphone-Based Controller. In IFIP Conference on Human-Computer Interaction (pp. 426-435). Cham: Springer Nature Switzerland.
Abstract:
A major concern in mixed-reality (MR) environments is to support intuitive and precise user interaction. Various modalities have been proposed and used, including gesture, gaze, voice, hand-recognition or even special devices, i.e. external controllers. However, these modalities may often feel unfamiliar and physically demanding to the end-user, leading to difficulties and fatigue. One possible solution worth investigating further is to use an everyday object, like a smartphone, as an external device for interacting with MR. In this paper, we present the design of a framework for developing an external smartphone controller to extend user input in MR applications, which we further utilize to implement a new interaction modality, a tap on the phone. We also report on findings of a user study (n=24) in which we examine performance and user experience of the suggested input modality through a comparative user evaluation task. The findings suggest that incorporating a smartphone as an external controller shows potential for enhancing user interaction in MR tasks requiring high precision, as well as pinpointing the value of providing alternative means of user input in MR applications depending on a given task and personalization aspects of an end-user.
Trigka, M., Papadoulis, G., Dritsas, E., & Fidas, C. (2023, August). Influences of Cognitive Styles on EEG-Based Activity: An Empirical Study on Visual Content Comprehension. In IFIP Conference on Human-Computer Interaction (pp. 496-500). Cham: Springer Nature Switzerland.
Abstract:
This paper presents an empirical study that examines how human cognitive style affects brain signal activity when individuals engage in a visual content comprehension task. To facilitate this study, we adopted an accredited cognitive style framework (Field Dependent-Field Independent or FD-FI) and utilized a validated cognitive style elicitation task, namely the Group Embedded Figures Test (GEFT), to elicit visual content comprehension via static figures. Brain signal activity was captured through a high-precision EEG device and subsequently correlated with the GEFT-derived cognitive style. Furthermore, power spectral analysis allowed the observation of potential differences between the two cognitive style groups. Analysis of results yields different effects on FD and FI users and especially in the average power of brain signals in the cortical area. Identifying such brain signal variations between FD-FI users might lay the ground for designing novel real-time elicitation frameworks of human cognitive styles, thus providing innovative personalization and adaptation approaches in a variety of application domains.
Trigka, Maria, Elias Dritsas, and Christos Fidas (2022, November) A Survey on Signal Processing Methods for EEG-based Brain Computer Interface Systems In Proceedings of the 26th Pan-Hellenic Conference on Informatics (pp. 213-218)
Abstract:
The development of human-computer interaction (HCI) systems that will efficiently capture the human brain, the so-called Brain-Computer Interaction (BCI) systems, will bring a new era in various disciplines (gaming, education, cultural heritage, etc). Actually, it is expected that the design and development of an electroencephalography (EEG) based-driven framework for intelligent real-time modelling of human cognitive abilities will provide groundbreaking technological advances in the delivery of human cognition-centred personalized systems and significantly advance the state-of-the-art research in human brain modelling. The aim of this paper is to make a concise and focused presentation of Signal Processing and Artificial Intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL), and how these fields may help to model and thus predict human behaviour, emotion, cognitive state in different tasks.
Thesis
The Department of Electrical Engineering and Computer Technology utilizes the CogniX Infrastructure for conducting master's thesesStudy of a PGA-EEG Authentication System Based on User Experiences Ioannis Katoikos [October 2023]
Design and development of an AI user authentication system leveraging on EEG data Foivos Chalantzoukas [October 2023]
Design and development of a user authentication system using electroencephalography (EEG) signals based on picture gesture authentication (PGA) environments Dimitrios Verginis [February 2024]