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Brainwave-based authentication using features fusion
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DOI：10.1016/j.cose.2023.103198.png)
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Abstract
En 中文
This article investigates the use of human brainwaves for user authentication. We used data collected from 50 volunteers and leveraged the Support Vector Machine (SVM) as a classification algorithm for the case study. User recognition patterns are taken from a combination of blinking, attention concentration, and picture recognition emotion sequences. These actions impact alpha, beta, gamma, and theta brain waves, which are measured using several electrodes. Ten different electrode placement patterns are ex-plored, with varied positioning on the head. For each placement position, four features are examined, for a total of 40 extracts in the learning model. Features are: 1) spectral information, 2) coherence, 3) mu-tual correlation coefficient, and 4) mutual information. Each feature type is trained by the SVM algorithm, and the 40 weak classifier candidates. Adaptive Boosting (AdaBoost), a type of machine learning, is then used to generate a robust classifier, which is subsequently used to create a model, and select features, used to accurately identify individuals for authentication purposes. Upon verifying the proposed method using 32 legitimate users and 18 intruders, we obtained an authentication error rate (ERR) of 0.52%, and a classification rate of 99.06%. & COPY; 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Keywords:
brainwaves
electroencephalogram
EEG
brain-computer interface
BCI
biometrics
authentication
machine learning
coherence
feature extraction
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