EEG-Based Personal Identification by Special Design Domain-Adaptive Autoencoder

Individual brain activity patterns derived from electroencephalogram (EEG) data offer a unique source for personal identification, introducing a novel approach to the field. Autoencoders are well-known machine learning models that automate feature extraction, which is a crucial step in biometric ide...

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Vydané v:Sensors (Basel, Switzerland) Ročník 25; číslo 20; s. 6457
Hlavní autori: Oztemel, Muhammed Esad, Soysal, Ömer Muhammet
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 18.10.2025
Predmet:
Adaptation
Adult
Algorithms
Autoencoder
biometric
Biometric identification
Biometric Identification - methods
Biometrics
Brain - physiology
Brain research
brainprint
Classification
Datasets
Deep learning
EEG
Electroencephalography
Electroencephalography - methods
fingerprint
Fourier transforms
Humans
identification
Machine Learning
Male
Neural networks
Signal Processing, Computer-Assisted
Support Vector Machine
Support vector machines
Wavelet transforms
autoencoder
identification
domain generation
EEG
fingerprint
brainprint
biometric
ISSN:1424-8220, 1424-8220
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Shrnutí:Individual brain activity patterns derived from electroencephalogram (EEG) data offer a unique source for personal identification, introducing a novel approach to the field. Autoencoders are well-known machine learning models that automate feature extraction, which is a crucial step in biometric identification. Among various types of autoencoders, the domain-adaptive autoencoder (DAAE) is explored for feature extraction. The extracted latent features are employed by four machine learning classifiers, KNN, ANN, SVM and RF, for personal identification. Two domain adaptation approaches were presented. The proposed frameworks were evaluated in a longitudinal setting, using three types of EEG recordings: resting state, auditory and cognitive stimuli. Model performance was assessed through experiments involving seven-, five- and two-subject classification tasks. The highest identification accuracy, 100%, was achieved by the SVM-based model in the two-subject experiment, using features extracted with the uniform referential DAAE. Similarly, the RF-based model attained an accuracy of 99.84% in the two-subject experiment when trained on features obtained from the softmin referential DAAE. As expected, accuracy declined with an increasing number of subjects in the dataset, reflecting the difficulty of multi-subject classification.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25206457