SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
02 Jul 2024
02 Jul 2024
Historique:
received:
11
12
2023
accepted:
18
06
2024
medline:
3
7
2024
pubmed:
3
7
2024
entrez:
2
7
2024
Statut:
epublish
Résumé
Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.
Identifiants
pubmed: 38956046
doi: 10.1038/s41597-024-03546-z
pii: 10.1038/s41597-024-03546-z
doi:
Types de publication
Journal Article
Dataset
Langues
eng
Sous-ensembles de citation
IM
Pagination
718Informations de copyright
© 2024. The Author(s).
Références
Jain, A., Hong, L. & Pankanti, S. Biometric identification. Communications of the ACM 43, 90–98 (2000).
doi: 10.1145/328236.328110
Tolosana, R. et al. Icdar 2021 competition on on-line signature verification. In Document Analysis and Recognition–ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part IV 16, 723–737 (Springer, 2021).
Tolosana, R. et al. Svc-ongoing: Signature verification competition. Pattern Recognition 127, 108609 (2022).
doi: 10.1016/j.patcog.2022.108609
Dutta, S., Saini, R., Kumar, P. & Roy, P. P. An efficient approach for recognition and verification of on-line signatures using pso. In 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), 882–887 (IEEE, 2017).
Palaniappan, R. Two-stage biometric authentication method using thought activity brain waves. International journal of neural systems 18, 59–66 (2008).
doi: 10.1142/S0129065708001373
pubmed: 18344223
Rodrigues, J. D. C., Filho, P. P. R., Damaeviius, R. & Albuquerque, V. H. C. EEG-based biometric systems. In Neurotechnology: Methods, advances and applications (2020).
Kumar, P., Saini, R., Kaur, B., Roy, P. P. & Scheme, E. Fusion of neuro-signals and dynamic signatures for person authentication. Sensors 19, 4641 (2019).
doi: 10.3390/s19214641
pubmed: 31661761
pmcid: 6864782
Saini, R. et al. Don’t just sign use brain too: A novel multimodal approach for user identification and verification. Information Sciences 430, 163–178 (2018).
doi: 10.1016/j.ins.2017.11.045
Inc, E. Insight User Manual — emotiv.gitbook.io. https://emotiv.gitbook.io/insight-manual/ [Accessed 06-Jun-2023] (2020).
Sharbrough, F. et al. American electroencephalographic society guidelines for standard electrode position nomenclature. Clinical Neurophysiology 8, 200–202 (1991).
doi: 10.1097/00004691-199104000-00007
Inc, E. Insight User Manual — emotiv.gitbook.io. https://emotiv.gitbook.io/emotivpro-v3/ [Accessed 06-Jun-2023] (2023).
Wacom. Wacom User Help (DTC133) — 101.wacom.com. http://101.wacom.com/UserHelp/en/TOC/DTC133.html [Accessed 06-Jun-2023] (2020).
Wacom. Signature scope - developer support. https://developer-support.wacom.com/hc/en-us/sections/9304704351895-Signature-Scope (Accessed on 06/06/2023). (2020).
Kosslyn, S. M., Thompson, W. L. & Ganis, G.The case for mental imagery. (Oxford University Press, New York, NY, US, 2006).
Ganis, G., Thompson, W. L. & Kosslyn, S. M. Brain areas underlying visual mental imagery and visual perception: an fmri study. Cognitive Brain Research 20, 226–241 (2004).
doi: 10.1016/j.cogbrainres.2004.02.012
pubmed: 15183394
Nanay, B. Mental Imagery (Stanford Encyclopedia of Philosophy) — plato.stanford.edu. https://plato.stanford.edu/entries/mental-imagery/#MentImagVsMotoImag [Accessed 09-May-2023] (1997).
Jeannerod, M. The representing brain: Neural correlates of motor intention and imagery. Behavioral and Brain sciences 17, 187–202 (1994).
doi: 10.1017/S0140525X00034026
Frenkel-Toledo, S., Bentin, S., Perry, A., Liebermann, D. G. & Soroker, N. Dynamics of the eeg power in the frequency and spatial domains during observation and execution of manual movements. Brain research 1509, 43–57 (2013).
doi: 10.1016/j.brainres.2013.03.004
pubmed: 23500633
Rimbert, S., Al-Chwa, R., Zaepffel, M. & Bougrain, L. Electroencephalographic modulations during an open-or closed-eyes motor task. PeerJ 6, e4492 (2018).
doi: 10.7717/peerj.4492
pubmed: 29576963
pmcid: 5857351
Taylor, P. C. J. & Thut, G. Brain activity underlying visual perception and attention as inferred from tms–eeg: A review. Brain stimulation 5, 124–129 (2012).
doi: 10.1016/j.brs.2012.03.003
pubmed: 22494831
Halim, N., Fuad, N., Marwan, M. & Nasir, E. Emotion state recognition using band power of eeg signals. In Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering: InECCE2021, Kuantan, Pahang, Malaysia, 23rd August, 939–950 (Springer, 2022).
Olivares-Figueroa, J. D., Cruz-Vega, I., Ramírez-Cortés, J., Gómez-Gil, P. & Martínez-Carranza, J. A compact approach for emotional assessment of drone pilots using bci. In 12th international micro air vehicle conference, Puebla, México, 57–62 (2021).
Zabcikova, M. Visual and auditory stimuli response, measured by emotiv insight headset. In MATEC Web of Conferences, vol. 292, 01024 (EDP Sciences, 2019).
Delorme, A. & Makeig, S. Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis. Journal of neuroscience methods 134, 9–21 (2004).
doi: 10.1016/j.jneumeth.2003.10.009
pubmed: 15102499
Mishra, A. R. et al. SignEEG v1.0 : Multimodal Electroencephalography and Signature Database for Biometric Systems. In Zenodo, https://doi.org/10.5281/zenodo.8332198 (2023).
Gribbon, K. T. & Bailey, D. G. A novel approach to real-time bilinear interpolation. In Proceedings. DELTA 2004. Second IEEE international workshop on electronic design, test and applications, 126–131 (IEEE, 2004).
Toa, C. K., Sim, K. S. & Tan, S. C. Emotiv insight with convolutional neural network: Visual attention test classification. In Advances in Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Kallithea, Rhodes, Greece, September 29–October 1, 2021, Proceedings 13, 348–357 (Springer, 2021).
Heunis, C. Export and analysis of emotiv insight eeg data via eeglab. Developing Alternative Stroke Rehabilitation to Reinforce Neural Pathways and Synapses of Middle Cerebral Artery Stroke Patients 2016, 1–11 (2016).
Zhang, H. et al. Eegdenoisenet: A benchmark dataset for deep learning solutions of eeg denoising. Journal of Neural Engineering 18, 056057 (2021).
doi: 10.1088/1741-2552/ac2bf8
Yu, J., Li, C., Lou, K., Wei, C. & Liu, Q. Embedding decomposition for artifacts removal in eeg signals. Journal of Neural Engineering 19, 026052 (2022).
doi: 10.1088/1741-2552/ac63eb
Saini, R. et al. Imagined object recognition using eeg-based neurological brain signals. In Recent Trends in Image Processing and Pattern Recognition, 305–319 (Springer International Publishing, 2022).
Hasan, M. A. M., Shin, J. & Maniruzzaman, M. Online kanji characters based writer identification using sequential forward floating selection and support vector machine. Applied Sciences 12, 10249 (2022).
doi: 10.3390/app122010249
Crasto, N. & Upadhyay, R. Wavelet decomposition based automatic sleep stage classification using eeg. In Bioinformatics and Biomedical Engineering: 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I 5, 508–516 (Springer, 2017).
Khalid, M., Yusof, R. & Mokayed, H. Fusion of multi-classifiers for online signature verification using fuzzy logic inference. International Journal of Innovative Computing 7, 2709–2726 (2011).
Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M. & Shenoy, K. V. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021).
doi: 10.1038/s41586-021-03506-2
pubmed: 33981047
pmcid: 8163299
Edelman, B. J. et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Science robotics 4, eaaw6844 (2019).
doi: 10.1126/scirobotics.aaw6844
pubmed: 31656937
pmcid: 6814169