Exploring the Feasibility to Authenticate Users of Web and Cloud Services Using a Brain-Computer Interface (BCI)

Orenda, Michael Philip; Garg, Lalit; Garg, Gaurav

doi:10.1007/978-3-319-70742-6_33

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The brain wave regarding the winks has been captured using the fives channel Emotiv Insight EEG headset. Emotiv Insight has also been employed in other studies [14][15][16][17][18][19] to capture the brain wave. The remaining part of this paper has been organized in the following sections i.e.…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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“…What is more, BCI creates a field for the development of new types of interfaces, and thus the opportunities for design of completely new quality of entertainment, mostly in regard to games (Kang et al , 2016; Vasiljevic et al , 2018), including so-called serious games which use the game mechanics as powerful teaching tool (Monaco et al , 2019). Of course, BCI is also used in the broadly understood commercial sector – both in industry and engineering (Angrisani et al , 2018), for instance in the context of using BCI to control robots or other mechanical elements of construction or production process (Hortal et al , 2015), as well as in business and services (Orenda et al , 2017); however, these applications are not still widely described in the scientific literature (Figure 7).…”

Section: Bci – Current Areas Of Usementioning

confidence: 99%

Brain–computer interface in the context of information retrieval systems in a library

Wójcik

2021

LHT

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PurposeThe subject of this paper is the idea of Brain–Computer Interface (BCI). The main goal is to assess the potential impact of BCI on the design, use and evaluation of information retrieval systems operating in libraries.Design/methodology/approachThe method of literature review was used to establish the state of research. The search according to accepted queries was carried out in the Scopus database and complementary in Google Scholar. To determine the state of research on BCI on the basis of library and information science, a specialist LISTA abstract database was also searched. The most current papers published in the years 2015–2019 in the English language or having at least an abstract in this language were taken into account.FindingsThe analysis showed that BCI issues are extremely popular in subject literature from various fields, mainly computer science, but practically does not occur in the context of using this technology in information retrieval systems.Research limitations/implicationsDue to the fact that BCI solutions are not yet implemented in libraries and are rarely the subject of scientific considerations in the field of library and information science, this article is mainly based on literature from other disciplines. The goal was to consider how much BCI solutions can affect library information retrieval systems. The considerations presented in this article are theoretical in nature due to the lack of empirical materials on which to base. The author's assumption was to initiate a discussion about BCI on the basis of library and information science, not to propose final solutions.Practical implicationsThe results can be widely used in practice as a framework for the implementation of BCI in libraries.Social implicationsThe article can help to facilitate the debate on the role of implementing new technologies in libraries.Originality/valueThe problem of BCI is very rarely addressed in the subject literature in the field of library and information science.

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“…2. A bandpass filter was chosen to be between 2-32Hz since theta, alpha and beta frequencies lie within this range [29]. It was implemented using the built-in EEGLAB 14 [26] feature 'Basic FIR Filter'.…”

Section: F Eeg Data Analysismentioning

confidence: 99%

“…Power Spectrum Density (PSD) estimate analysis was used for feature extraction from the EEG data. It was due to multiple studies [27], [30]- [32] which have successfully established a correlation between the different frequency bands (theta, alpha, and beta) in EEG signals and emotions [29]. PSD transformed the EEG data from time-domain to frequency-domain [33].…”

Section: ) Processing Stagementioning

confidence: 99%

“…In the following test, we calculated each respective participant's theta, alpha, and beta spectral power bands and compared both groups [29]. Consequently, for every respective participant, we had twenty tasks multiplied by sixteen channels totalling three hundred thirty six features for each of the three frequency bands, i.e.…”

Section: Theta Alpha and Beta Frequencies In Each Channelmentioning

confidence: 99%

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Emotional Testing on Facebook’s User Experience

Mangion

Garg

et al. 2020

IEEE Access

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This study aims at understanding how a user's emotions fluctuate when undertaking certain tasks on a social media platform such as Facebook or other software products which may have emotional effects on its user. Specifically, we explored the difference in the usability aspect of Facebook concerning frequent and new Facebook users. The study involves a qualitative study on eighteen participants, nine of whom were Facebook users and nine non-Facebook users who had never used Facebook before participating in this study. During the testing procedure, users were asked to complete several tasks on Facebook, while the electrophysiological activity of their brain was recorded using an EEG (electroencephalogram) acquisition system. Certainly, this study can be applied to any software product, before its release, to improve its user interface by acquiring insight into how user-friendly it is for new users when compared to frequent users. Additionally, a correlation in user friendliness between new users and frequent users is investigated. Furthermore, the study will help us discern which parts of the brain had the most significant difference between groups and discuss the motives behind an individual's emotional state, concerning user experience. Based on the analysis of the power spectrum of the characteristic brain waves, this research establishes that there is a substantial statistical difference between new and frequent Facebook users. Also, it resulted that there is a significant difference between the central, temporal and occipital lobes of new and frequent users. These results will assist developers in creating optimal and user-friendly software products. INDEX TERMS Social systems' behavior analysis, man-machine interaction, social systems design, emotion recognition, human-computer interaction, user experience, behavioral aspects in computational modeling, psychological factors in software design, usability testing, technology evaluation, modeling human emotions, neuroimaging, digital signal processing, EEG, testing tools, Facebook.

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Exploring the Feasibility to Authenticate Users of Web and Cloud Services Using a Brain-Computer Interface (BCI)

Cited by 8 publications

References 14 publications

Wink based facial expression classification using machine learning approach

Wink based facial expression classification using machine learning approach

Brain–computer interface in the context of information retrieval systems in a library

Emotional Testing on Facebook’s User Experience

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