Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results
Abstract
:1. Introduction
2. Methodology
2.1. Data Collection
2.2. Preprocessing and Features Extraction
2.3. Data Set Creation and Results Evaluation
3. Results of the Experiment
3.1. Training Sessions: 1–15, Testing Sessions: 16–20
3.2. Optimal Number of Training Sessions
3.3. Results for Training Sessions 1–8 and Testing Sessions 9–20
3.4. Simulated Impostor Attack
4. Discussion
5. Conclusions
- Using the same recordings for training and testing may cause data leakage, resulting in false high classification accuracy.
- One session may not reflect the technical and environmental conditions encountered in various practical situations as the practical application of the considered EEG-based method involves multiple sessions that bring additional variance due to differences in electrode montage, impedance, and the mental state of a subject. None of these sources of variance is accessible to machine learning procedures worked out from one session’s data set.
- In practical applications, some electrodes may not contact properly and not always in the same way.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Berger, H. Über das Elektrenkephalogramm des Menschen. Arch. Psychiatr. Nervenkr. 1929, 87, 527–570. [Google Scholar] [CrossRef]
- Viviani, G.; Vallesi, A. EEG-neurofeedback and executive function enhancement in healthy adults: A systematic review. Psychophysiology 2021, 58, e13874. [Google Scholar] [CrossRef] [PubMed]
- Torres, E.P.; Torres, E.A.; Hernández-Álvarez, M.; Yoo, S.G. EEG-Based BCI Emotion Recognition: A Survey. Sensors 2020, 20, 5083. [Google Scholar] [CrossRef] [PubMed]
- Keune, P.M.; Hansen, S.; Weber, E.; Zapf, F.; Habich, J.; Muenssinger, J.; Wolf, S.; Schönenberg, M.; Oschmann, P. Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis. Clin. Neurophysiol. 2017, 128, 1746–1754. [Google Scholar] [CrossRef]
- Aboalayon, K.A.I.; Faezipour, M.; Almuhammadi, W.S.; Moslehpour, S. Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation. Entropy 2016, 18, 272. [Google Scholar] [CrossRef]
- Acharya, U.R.; Vinitha Sree, S.; Swapna, G.; Martis, R.J.; Suri, J.S. Automated EEG analysis of epilepsy: A review. Knowl. Based Syst. 2013, 45, 147–165. [Google Scholar] [CrossRef]
- Malekzadeh, A.; Zare, A.; Yaghoobi, M.; Kobravi, H.-R.; Alizadehsani, R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors 2021, 21, 7710. [Google Scholar] [CrossRef]
- Eagleman, S.L.; Drover, D.R. Calculations of consciousness: Electroencephalography analyses to determine anesthetic depth. Curr. Opin. Anaesthesiol. 2018, 31, 431–438. [Google Scholar] [CrossRef]
- Kautzky, A.; Vanicek, T.; Philippe, C.; Kranz, G.S.; Wadsak, W.; Mitterhauser, M.; Hartmann, A.; Hahn, A.; Hacker, M.; Rujescu, D.; et al. Machine learning classification of ADHD and HC by multimodal serotonergic data. Transl. Psychiatry 2020, 10, 104. [Google Scholar] [CrossRef]
- Shoeibi, A.; Sadeghi, D.; Moridian, P.; Ghassemi, N.; Heras, J.; Alizadehsani, R.; Khadem, A.; Kong, Y.; Nahavandi, S.; Zhang, Y.-D.; et al. Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Front. Neuroinform. 2021, 15, 777977. [Google Scholar] [CrossRef]
- ISO/IEC 2382-37:2022; Information Technology—Vocabulary—Part 37: Biometrics. 3rd ed. ISO/IEC: Geneva, Switzerland, 2022.
- Poulos, M.; Rangoussi, M.; Alexandris, N. Neural network based person identification using EEG features. In Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP99 (Cat. No.99CH36258), Phoenix, AZ, USA, 15–19 March 1999; Volume 2, pp. 1117–1120. [Google Scholar]
- Poulos, M.; Rangoussi, M.; Chrissikopoulos, V.; Evangelou, A. Person identification based on parametric processing of the EEG. In Proceedings of the ICECS’99, 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), Paphos, Cyprus, 5–8 September 1999; Volume 1, pp. 283–286. [Google Scholar]
- Poulos, M.; Rangoussi, M.; Chrissikopoulos, V.; Evangelou, A. Parametric person identification from the EEG using computational geometry. In Proceedings of the ICECS’99, 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), Paphos, Cyprus, 5–8 September 1999; Volume 2, pp. 1005–1008. [Google Scholar]
- Palaniappan, R.; Mandic, D.P. Biometrics from brain electrical activity: A machine learning approach. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29, 738–742. [Google Scholar] [CrossRef] [PubMed]
- Abdullah, M.K.; Subari, K.S.; Loong, J.L.C.; Ahmad, N.N. Analysis of the EEG Signal for a Practical Biometric System. World Acad. Sci. Eng. Technol. 2010, 44, 1133–1137. [Google Scholar] [CrossRef]
- Das, R.; Maiorana, E.; Campisi, P. EEG Biometrics Using Visual Stimuli: A Longitudinal Study. IEEE Signal Process. Lett. 2016, 23, 341–345. [Google Scholar] [CrossRef]
- Arias-Cabarcos, P.; Habrich, T.; Becker, K.; Becker, C.; Strufe, T. Inexpensive Brainwave Authentication: New Techniques and Insights on User Acceptance. In Proceedings of the 30th USENIX Security Symposium (USENIX Security 21), 11–13 August 2021; pp. 55–72. [Google Scholar]
- Chan, H.-L.; Kuo, P.-C.; Cheng, C.-Y.; Chen, Y.-S. Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition. Front. Neuroinform. 2018, 12, 66. [Google Scholar] [CrossRef]
- Goudiaby, B.; Othmani, A.; Nait-ali, A. EEG Biometrics for Person Verification. In Hidden Biometrics: When Biometric Security Meets Biomedical Engineering; Nait-ali, A., Ed.; Springer: Singapore, 2020; pp. 45–69. ISBN 978-981-13-0956-4. [Google Scholar]
- Abo-Zahhad, M.; Ahmed, S.M.; Abbas, S.N. State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals. IET Biometrics 2015, 4, 179–190. [Google Scholar] [CrossRef]
- Ma, L.; Minett, J.W.; Blu, T.; Wang, W.S.-Y. Resting State EEG-based biometrics for individual identification using convolutional neural networks. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 25–29 August 2015; pp. 2848–2851. [Google Scholar]
- Wang, M.; Yin, X.; Zhu, Y.; Hu, J. Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey. Sensors 2022, 22, 5111. [Google Scholar] [CrossRef]
- Jayarathne, I.; Cohen, M.; Amarakeerthi, S. BrainID: Development of an EEG-based biometric authentication system. In Proceedings of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 13–15 October 2016; pp. 1–6. [Google Scholar]
- DelPozo-Banos, M.; Travieso, C.M.; Weidemann, C.T.; Alonso, J.B. EEG biometric identification: A thorough exploration of the time-frequency domain. J. Neural Eng. 2015, 12, 056019. [Google Scholar] [CrossRef]
- Miladinović, A.; Ajčević, M.; Jarmolowska, J.; Marusic, U.; Colussi, M.; Silveri, G.; Battaglini, P.P.; Accardo, A. Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study. Comput. Methods Programs Biomed. 2021, 198, 105808. [Google Scholar] [CrossRef]
- Raza, H.; Rathee, D.; Zhou, S.-M.; Cecotti, H.; Prasad, G. Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface. Neurocomputing 2019, 343, 154–166. [Google Scholar] [CrossRef]
- Di, Y.; An, X.; He, F.; Liu, S.; Ke, Y.; Ming, D. Robustness Analysis of Identification Using Resting-State EEG Signals. IEEE Access 2019, 7, 42113–42122. [Google Scholar] [CrossRef]
- Kim, D.; Kim, K. Resting State EEG-Based Biometric System Using Concatenation of Quadrantal Functional Networks. IEEE Access 2019, 7, 65745–65756. [Google Scholar] [CrossRef]
- Lai, C.Q.; Ibrahim, H.; Abdullah, M.Z.; Abdullah, J.M.; Suandi, S.A.; Azman, A. Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification. Comput. Intell. Neurosci. 2019, 2019, 7895924. [Google Scholar] [CrossRef] [PubMed]
- Maiorana, E.; Campisi, P. Longitudinal Evaluation of EEG-Based Biometric Recognition. IEEE Trans. Inf. Forensics Secur. 2018, 13, 1123–1138. [Google Scholar] [CrossRef]
- Maiorana, E. Learning deep features for task-independent EEG-based biometric verification. Pattern Recognit. Lett. 2021, 143, 122–129. [Google Scholar] [CrossRef]
- Chen, Y.; Atnafu, A.D.; Schlattner, I.; Weldtsadik, W.T.; Roh, M.-C.; Kim, H.J.; Lee, S.-W.; Blankertz, B.; Fazli, S. A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes. IEEE Trans. Inf. Forensics Secur. 2016, 11, 2635–2647. [Google Scholar] [CrossRef]
- Bashar, M.K.; Chiaki, I.; Yoshida, H. Human identification from brain EEG signals using advanced machine learning method EEG-based biometrics. In Proceedings of the 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia, 4–8 December 2016; pp. 475–479. [Google Scholar]
- Jijomon, C.M.; Vinod, A.P. EEG-based Biometric Identification using Frequently Occurring Maximum Power Spectral Features. In Proceedings of the 2018 IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, 7–9 December 2018; pp. 249–252. [Google Scholar]
- Thomas, K.P.; Vinod, A.P. EEG-Based Biometric Authentication Using Gamma Band Power During Rest State. Springer 2018, 37, 277–289. [Google Scholar] [CrossRef]
- Ruiz-Blondet, M.V.; Jin, Z.; Laszlo, S. CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification. IEEE Trans. Inf. Forensics Secur. 2016, 11, 1618–1629. [Google Scholar] [CrossRef]
- Nakanishi, I.; Baba, S.; Miyamoto, C. EEG based biometric authentication using new spectral features. In Proceedings of the 2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Kanazawa, Japan, 7–9 January 2009; pp. 651–654. [Google Scholar]
- Campisi, P.; Rocca, D. La Brain waves for automatic biometric-based user recognition. IEEE Trans. Inf. Forensics Secur. 2014, 9, 782–800. [Google Scholar] [CrossRef]
- Yang, S.; Deravi, F. On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey. IEEE Trans. Hum. Mach. Syst. 2017, 47, 958–969. [Google Scholar] [CrossRef]
- Plucińska, R.; Jędrzejewski, K.; Waligóra, M.; Malinowska, U.; Rogala, J. Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks. Sensors 2022, 22, 5529. [Google Scholar] [CrossRef]
- Jasper, H.H. Report of the committee on methods of clinical examination in electroencephalography: 1957. Electroencephalogr. Clin. Neurophysiol. 1958, 10, 370–375. [Google Scholar] [CrossRef]
- Van Beijsterveldt, C.E.M.; Molenaar, P.C.M.; de Geus, E.J.C.; Boomsma, D.I. Heritability of human brain functioning as assessed by electroencephalosraphy. Am. J. Hum. Genet. 1996, 58, 562–573. [Google Scholar] [PubMed]
- Smit, D.J.A.; Stam, C.J.; Posthuma, D.; Boomsma, D.I.; de Geus, E.J.C. Heritability of “Small-World” Networks in the Brain: A Graph Theoretical Analysis of Resting-State EEG Functional Connectivity. Hum. Brain Mapp. 2008, 29, 1368–1378. [Google Scholar] [CrossRef]
- Biswal, B.; Yetkin, F.Z.; Haughton, V.M.; Hyde, J.S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 1995, 34, 537–541. [Google Scholar] [CrossRef] [PubMed]
- McFarland, D.J.; McCane, L.M.; David, S.V.; Wolpaw, J.R. Spatial filter selection for EEG-based communication. Electroencephalogr. Clin. Neurophysiol. 1997, 103, 386–394. [Google Scholar] [CrossRef] [PubMed]
- Basheer, I.A.; Hajmeer, M. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 2000, 43, 3–31. [Google Scholar] [CrossRef] [PubMed]
- Hagan, M.T.; Menhaj, M.B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 1994, 5, 989–993. [Google Scholar] [CrossRef] [PubMed]
- Jalaly Bidgoly, A.; Jalaly Bidgoly, H.; Arezoumand, Z. A survey on methods and challenges in EEG based authentication. Comput. Secur. 2020, 93, 101788. [Google Scholar] [CrossRef]
- Chen, J.X.; Mao, Z.J.; Yao, W.X.; Huang, Y.F. EEG-based biometric identification with convolutional neural network. Multimed. Tools Appl. 2020, 79, 10655–10675. [Google Scholar] [CrossRef]
- Özdenizci, O.; Wang, Y.; Koike-Akino, T.; Erdoğmuş, D. Adversarial Deep Learning in EEG Biometrics. IEEE Signal Process. Lett. 2019, 26, 710–714. [Google Scholar] [CrossRef]
- Paluch, K.; Jurewicz, K.; Rogala, J.; Krauz, R.; Szczypińska, M.; Mikicin, M.; Wróbel, A.; Kublik, E. Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies. Front. Hum. Neurosci. 2017, 11, 119. [Google Scholar] [CrossRef] [PubMed]
- Wang, M.; Wang, S.; Hu, J. Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems. IEEE Trans. Inf. Forensics Secur. 2022, 17, 3350–3364. [Google Scholar] [CrossRef]
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC [% ± SD] | 75.3 ±10.5 | 80.6 ±8.8 | 83.3 ±9.1 | 85.6 ±7.3 | 86.2 ±7.4 | 87.7 ±7.5 | 89.5 ±7.0 | 89.4 ±7.2 | 90.9 ±5.8 | 90.9 ±6.8 | 91.1 ±6.8 | 91.3 ±6.3 | 91.8 ±5.6 | 92.2 ±6.3 | 92.6 ±5.6 |
SEN [% ± SD] | 69.5 ±21.3 | 76.9 ±17.9 | 79.4 ±16.3 | 81.9 ±13.7 | 83.8 ±13.4 | 85.6 ±13.6 | 87.6 ±11.7 | 87.6 ±12.2 | 90.3 ±10.8 | 89.2 ±12.1 | 90.7 ±11.2 | 90.8 ±10.5 | 91.3 ±9.0 | 92.1 ±10.2 | 92.2 ±9.4 |
SPEC [% ± SD] | 81.2 ±7.9 | 84.3 ±6.8 | 87.2 ±5.4 | 89.3 ±4.4 | 88.6 ±5.1 | 89.9 ±3.9 | 91.4 ±4.2 | 91.2 ±4.2 | 91.5 ±3.4 | 92.5 ±2.8 | 91.4 ±3.7 | 91.8 ±3.0 | 92.4 ±3.4 | 92.2 ±4.0 | 93.0 ±3.5 |
PREC [% ± SD] | 77.1 ±11.1 | 82.9 ±6.6 | 85.8 ±6.6 | 88.4 ±5.0 | 88.1 ±5.5 | 89.3 ±4.7 | 91.0 ±4.8 | 90.7 ±5.0 | 91.3 ±3.5 | 92.0 ±3.8 | 91.2 ±4.5 | 91.5 ±3.8 | 92.2 ±3.8 | 92.1 ±4.4 | 92.9 ±3.7 |
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC [% ± SD] | 79.1 ±10.7 | 87.0 ±8.3 | 89.3 ±7.6 | 91.2 ±6.9 | 92.8 ±6.1 | 93.7 ±6.0 | 95.0 ±4.8 | 95.3 ±4.7 | 95.2 ±5.7 | 95.8 ±4.2 | 95.8 ±4.8 | 96.0 ±4.9 | 96.1 ±5.0 | 96.8 ±4.0 | 96.7 ±4.2 |
SEN [% ± SD] | 71.4 ±20.6 | 81.3 ±15.6 | 84.1 ±15.0 | 86.7 ±13.0 | 89.0 ±12.3 | 89.8 ±11.0 | 92.8 ±8.9 | 93.2 ±8.6 | 92.6 ±10.6 | 93.9 ±7.9 | 93.3 ±9.1 | 93.9 ±9.0 | 94.1 ±9.3 | 95.0 ±7.4 | 94.8 ±8.0 |
SPEC [% ± SD] | 86.8 ±7.4 | 92.6 ±3.7 | 94.4 ±4.4 | 95.7 ±2.5 | 96.7 ±1.9 | 97.6 ±1.8 | 97.3 ±2.0 | 97.5 ±1.7 | 97.8 ±1.5 | 97.8 ±1.6 | 98.2 ±1.2 | 98.1 ±1.3 | 98.1 ±1.4 | 98.5 ±1.6 | 98.5 ±1.1 |
PREC [% ± SD] | 83.1 ±10.6 | 91.5 ±4.7 | 94.0 ±4.3 | 95.2 ±3.1 | 96.5 ±1.9 | 97.3 ±2.1 | 97.1 ±2.2 | 97.4 ±1.8 | 97.6 ±1.8 | 97.7 ±1.6 | 98.1 ±1.4 | 98.0 ±1.5 | 98.0 ±1.5 | 98.4 ±1.6 | 98.5 ±1.1 |
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
p-val. | 0.613 | 0.317 | 0.276 | 0.153 | 0.141 | 0.197 | 0.138 | 0.123 | 0.027 | 0.092 | 0.040 | 0.012 | 0.028 | 0.013 | 0.028 |
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
2 | 0.343 | 0.020 | 0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | ||
3 | 0.202 | 0.042 | 0.006 | 0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |||
4 | 0.213 | 0.056 | 0.005 | 0.002 | 0.001 | 0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | ||||
5 | 0.494 | 0.109 | 0.048 | 0.040 | 0.019 | 0.011 | 0.003 | 0.003 | 0.001 | 0.001 | |||||
6 | 0.343 | 0.231 | 0.150 | 0.120 | 0.078 | 0.040 | 0.017 | 0.005 | 0.007 | ||||||
7 | 0.738 | 0.549 | 0.460 | 0.273 | 0.197 | 0.077 | 0.033 | 0.047 | |||||||
8 | 0.721 | 0.597 | 0.460 | 0.259 | 0.234 | 0.090 | 0.126 | ||||||||
9 | 0.774 | 0.715 | 0.388 | 0.247 | 0.162 | 0.186 | |||||||||
10 | 0.791 | 0.455 | 0.384 | 0.191 | 0.216 | ||||||||||
11 | 0.732 | 0.499 | 0.269 | 0.401 | |||||||||||
12 | 0.663 | 0.465 | 0.414 | ||||||||||||
13 | 0.852 | 0.834 | |||||||||||||
14 | 1.000 | ||||||||||||||
15 |
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
ACC [% ± SD] | 79.7 ±8.4 | 88.2 ±6.9 | 90.3 ±6.7 | 91.5 ±6.3 | 92.6 ±5.4 | 93.8 ±4.8 | 94.4 ±4.3 | 94.9 ±4.6 |
SEN [% ± SD] | 70.7 ±17.8 | 82.7 ±13.3 | 86.4 ±13.7 | 87.3 ±12.4 | 89.4 ±10.3 | 90.7 ±9.5 | 91.5 ±8.4 | 92.4 ±8.5 |
SPEC [% ± SD] | 88.8 ±5.2 | 93.6 ±2.7 | 94.2 ±3.6 | 95.7 ±2.3 | 95.8 ±2.1 | 96.9 ±1.6 | 97.3 ±1.5 | 97.3 ±1.8 |
PREC [% ± SD] | 86.1 ±5.8 | 92.9 ±3.1 | 94.0 ±3.4 | 95.4 ±2.5 | 95.6 ±2.2 | 96.7 ±1.6 | 97.2 ±1.5 | 97.2 ±1.9 |
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
FAR [% ± SD] | 13.9 ±7.0 | 8.5 ±4.6 | 7.1 ±4.7 | 6.3 ±3.7 | 5.3 ±3.4 | 4.4 ±3.3 | 4.6 ±3.3 | 4.4 ±2.5 | 3.9 ±3.1 | 3.9 ±2.7 | 3.7 ±2.7 | 3.5 ±2.8 | 3.5 ±2.9 | 3.6 ±3.1 | 3.1 ±2.2 |
Session No. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
2 | 0.111 | 0.047 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | ||
3 | 0.803 | 0.140 | 0.008 | 0.015 | 0.012 | 0.003 | 0.002 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | |||
4 | 0.237 | 0.031 | 0.042 | 0.042 | 0.006 | 0.006 | 0.007 | 0.002 | 0.001 | 0.002 | 0.001 | ||||
5 | 0.166 | 0.331 | 0.320 | 0.044 | 0.050 | 0.055 | 0.014 | 0.021 | 0.021 | 0.004 | |||||
6 | 0.709 | 0.441 | 0.504 | 0.680 | 0.555 | 0.297 | 0.308 | 0.283 | 0.237 | ||||||
7 | 0.901 | 0.305 | 0.423 | 0.320 | 0.124 | 0.179 | 0.202 | 0.084 | |||||||
8 | 0.240 | 0.308 | 0.191 | 0.074 | 0.111 | 0.176 | 0.023 | ||||||||
9 | 0.709 | 0.938 | 0.780 | 0.780 | 0.658 | 0.732 | |||||||||
10 | 0.756 | 0.446 | 0.534 | 0.428 | 0.290 | ||||||||||
11 | 0.732 | 0.774 | 0.709 | 0.555 | |||||||||||
12 | 0.803 | 0.876 | 0.810 | ||||||||||||
13 | 0.901 | 0.641 | |||||||||||||
14 | 0.669 | ||||||||||||||
15 |
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Plucińska, R.; Jędrzejewski, K.; Malinowska, U.; Rogala, J. Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results. Sensors 2023, 23, 2057. https://doi.org/10.3390/s23042057
Plucińska R, Jędrzejewski K, Malinowska U, Rogala J. Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results. Sensors. 2023; 23(4):2057. https://doi.org/10.3390/s23042057
Chicago/Turabian StylePlucińska, Renata, Konrad Jędrzejewski, Urszula Malinowska, and Jacek Rogala. 2023. "Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results" Sensors 23, no. 4: 2057. https://doi.org/10.3390/s23042057
APA StylePlucińska, R., Jędrzejewski, K., Malinowska, U., & Rogala, J. (2023). Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results. Sensors, 23(4), 2057. https://doi.org/10.3390/s23042057