Effect of Auditory Stimuli on Electroencephalography-based Authentication

Alzahab, Nibras Abo; Iorio, Angelo Di; Baldi, Marco; Scalise, Lorenzo

doi:10.1109/metroxraine54828.2022.9967652

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…To demonstrate the application of the proposed method in practical data, the signals of auditory-evoked potential EEG-biometric dataset [49] are analysed to investigate the inter-channel interaction under various types of auditory stimulation conditions. The dataset can be downloaded from the website: .…”

Section: Analysis and Resultsmentioning

confidence: 99%

Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot

Chen

et al. 2023

R. Soc. open sci.

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To evaluate the synchronization of bivariate time series has been a hot topic, and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network into the crossplot, a new method for measuring the synchronization of bivariate time series is proposed. After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitability for short time series. Finally, electroencephalogram (EEG) data from auditory-evoked potential EEG-biometric dataset are investigated, and some useful and interesting results are obtained.

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Section: Analysis and Resultsmentioning

confidence: 99%

Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot

Chen

et al. 2023

R. Soc. open sci.

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“…In conclusion, there is a need for sustainable and decentralized technologies for data storage and sharing based on distributed ledger technologies aiming to increase scalability of biometric data [111,112]. In addition, there is a need for standardized data models that describe in a holistic manner static and dynamic contextual data and personal biometric-driven data that will coherently reflect end-users based on semantic-based meta-data descriptions.…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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The employment of Electroencephalography (EEG) in the User Authentication (UA) scientific research has recently unlocked state-of-the-art experimentation, aiming at identifying and authenticating individuals given their brainiac activity within specific contexts of use. Indeed, utilizing EEG signals that are derived from brainiac activities can be used for tackling existing UA security threats such as shoulder surfing, thus providing a novel solution to contemporary security problems in traditional knowledge-based user authentication. In this survey, we aim to complete previous literature surveys by providing a systematic classification and presentation of existing research that is based on the following pillars: a) the user experimental setup, with an emphasis on the applied EEG-acquisition protocols (e.g., rest, external stimuli driven, mental and hybrid); b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad area of experiment protocols and various algorithms used for EEG signal classification. Moreover, most cited works include results from more than one experiments with different 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.

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Assessing Authentication Performance in EEG Recordings: An Information Theory Metrics Approach Across Experimental Conditions

Quartana,

Barletta,

Caputo

et al. 2024

2024 18th International Symposium on Medical Information and Communication Technology (ISMICT)

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Effect of Auditory Stimuli on Electroencephalography-based Authentication

Cited by 5 publications

References 11 publications

Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot

Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Assessing Authentication Performance in EEG Recordings: An Information Theory Metrics Approach Across Experimental Conditions

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