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Hernández-Álvarez L, Barbierato E, Caputo S, Mucchi L, Hernández Encinas L. EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers. Sensors (Basel) 2022; 23:186. [PMID: 36616785 PMCID: PMC9823500 DOI: 10.3390/s23010186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user's inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and χ2 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%.
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Affiliation(s)
- Luis Hernández-Álvarez
- Computer Security Lab, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Institute of Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, Spain
| | - Elena Barbierato
- Department of Agriculture, Food, Environment and Forestry, University of Florence, 50144 Firenze, Italy
| | - Stefano Caputo
- Department of Information Engineering, University of Florence, 50139 Firenze, Italy
| | - Lorenzo Mucchi
- Department of Information Engineering, University of Florence, 50139 Firenze, Italy
| | - Luis Hernández Encinas
- Institute of Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, Spain
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Santoyo-Ramón JA, Casilari E, Cano-García JM. A Study of One-Class Classification Algorithms for Wearable Fall Sensors. Biosensors (Basel) 2021; 11:284. [PMID: 34436087 DOI: 10.3390/bios11080284] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/10/2021] [Accepted: 08/14/2021] [Indexed: 11/22/2022]
Abstract
In recent years, the popularity of wearable devices has fostered the investigation of automatic fall detection systems based on the analysis of the signals captured by transportable inertial sensors. Due to the complexity and variety of human movements, the detection algorithms that offer the best performance when discriminating falls from conventional Activities of Daily Living (ADLs) are those built on machine learning and deep learning mechanisms. In this regard, supervised machine learning binary classification methods have been massively employed by the related literature. However, the learning phase of these algorithms requires mobility patterns caused by falls, which are very difficult to obtain in realistic application scenarios. An interesting alternative is offered by One-Class Classifiers (OCCs), which can be exclusively trained and configured with movement traces of a single type (ADLs). In this paper, a systematic study of the performance of various typical OCCs (for diverse sets of input features and hyperparameters) is performed when applied to nine public repositories of falls and ADLs. The results show the potentials of these classifiers, which are capable of achieving performance metrics very similar to those of supervised algorithms (with values for the specificity and the sensitivity higher than 95%). However, the study warns of the need to have a wide variety of types of ADLs when training OCCs, since activities with a high degree of mobility can significantly increase the frequency of false alarms (ADLs identified as falls) if not considered in the data subsets used for training.
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Monteiro PI, Santos JS, Rodionova OY, Pomerantsev A, Chaves ES, Rosso ND, Granato D. Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling. J Food Sci 2019; 84:3099-3108. [PMID: 31645089 DOI: 10.1111/1750-3841.14815] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 08/14/2019] [Accepted: 08/16/2019] [Indexed: 11/28/2022]
Abstract
In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation. Similarly, linear discriminant analysis was able to discriminate 100% and 81% of ORG and CONV coffees in the external validation, respectively, in which total phenolic content (TPC), ferric reducing antioxidant activity, and caffeic acid were the main discriminant variables. Overall 100% of samples from Paraná, Minas Gerais, and blended samples were correctly classified, where TPC, flavonoids, inhibition of lipid peroxidation, caffeic acid, pH, and soluble solids were the main discriminant variables. Support vector machines classified 95% ORG and 88% CONV, 100% Coffea arabica, and 88% and 78% coffees produced in São Paulo and Minas Gerais. k-Nearest neighbors was effective in distinguishing 100% CONV, 89% ORG, 100% coffees from São Paulo, and 100% C. arabica coffees. Overall, HPLC data and simple physicochemical parameters allied to chemometrics were effective in authenticating the cultivation system and the botanical origin of Brazilian coffees. PRACTICAL APPLICATION: Coffee adulteration is a serious problem in the food chain as some fraudsters replace coffee powder by other cheaper products. In the case of organic coffee, this scenario is even worse as still there is not a universal method to differentiate conventionally grown coffee from its organic counterpart. In addition, Brazilian coffee is produced in different regions and the commercial value varies. Therefore, we analyzed some physicochemical, chemical, and antioxidant properties of Brazilian coffees from distinct origins and classified the samples using chemometrics. Our approach seems to be interesting for quality control purposes.
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Affiliation(s)
- Pablo Inocêncio Monteiro
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil
| | - Jânio Sousa Santos
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil
| | - Oxana Ye Rodionova
- Semenov Inst. of Chemical Physics, Russian Academy of Sciences, Moscow, 119991, Russia.,Branch of Inst. of Natural and Technical Systems, Russian Academy of Sciences, Sochi, 354024, Russia
| | - Alexey Pomerantsev
- Semenov Inst. of Chemical Physics, Russian Academy of Sciences, Moscow, 119991, Russia.,Branch of Inst. of Natural and Technical Systems, Russian Academy of Sciences, Sochi, 354024, Russia
| | - Eduardo Sidinei Chaves
- Dept. of Chemistry, Federal Univ. of Santa Catarina, Florianópolis, Santa Catarina, 88040-900, Brazil
| | - Neiva Deliberali Rosso
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil
| | - Daniel Granato
- Graduation Program in Food Science and Technology, State Univ. of Ponta Grossa, Ponta Grossa, Paraná, 84030-900, Brazil.,Author Granato is also with Food Processing and Quality, Innovative Food System, Production Systems Unit-Natural Resources Inst. Finland (Luke), Espoo, FI-02150, Finland
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Granato D, Putnik P, Kovačević DB, Santos JS, Calado V, Rocha RS, Cruz AGD, Jarvis B, Rodionova OY, Pomerantsev A. Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing. Compr Rev Food Sci Food Saf 2018; 17:663-677. [PMID: 33350122 DOI: 10.1111/1541-4337.12341] [Citation(s) in RCA: 246] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/25/2018] [Accepted: 01/26/2018] [Indexed: 11/27/2022]
Abstract
In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.
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Affiliation(s)
- Daniel Granato
- Dept. of Food Engineering, State Univ. of Ponta Grossa, Av. Carlos Cavalcanti, 4748, 84030-900, Ponta Grossa, Brazil
| | - Predrag Putnik
- Faculty of Food Technology and Biotechnology, Univ. of Zagreb, Pierottijeva 6, 10000, Zagreb, Croatia
| | - Danijela Bursać Kovačević
- Faculty of Food Technology and Biotechnology, Univ. of Zagreb, Pierottijeva 6, 10000, Zagreb, Croatia
| | - Jânio Sousa Santos
- Dept. of Food Engineering, State Univ. of Ponta Grossa, Av. Carlos Cavalcanti, 4748, 84030-900, Ponta Grossa, Brazil
| | - Verônica Calado
- School of Chemistry, Federal Univ. of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ramon Silva Rocha
- Dept. de Alimentos, Inst. Federal de Educação, Ciência e Tecnologia (IFRJ), 20270-021, Rio de Janeiro, Brazil
| | - Adriano Gomes Da Cruz
- Dept. de Alimentos, Inst. Federal de Educação, Ciência e Tecnologia (IFRJ), 20270-021, Rio de Janeiro, Brazil
| | - Basil Jarvis
- Dept. of Food and Nutrition Sciences, School of Chemistry, Food and Pharmacy, The Univ. of Reading, Whiteknights, Reading, Berkshire RG6 6AP, U.K
| | - Oxana Ye Rodionova
- Semenov Inst. of Chemical Physics RAS, Kosygin str. 4, 119991, Moscow, Russia
| | - Alexey Pomerantsev
- Semenov Inst. of Chemical Physics RAS, Kosygin str. 4, 119991, Moscow, Russia
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