Person Identification from the EEG using Nonlinear Signal Classification

Poulos, Marios; Rangoussi, Maria; Alexandris, Nikolaos; Evangelou, Angelos

doi:10.1055/s-0038-1634316

Cited by 122 publications

(13 citation statements)

References 28 publications

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“…The sample size of our study, while small, is comparable to that of other EEG authentication studies (Poulos et al, 2002; Marcel and Millan, 2007; Ashby et al, 2011; Chuang et al, 2013; Curran et al, 2016) and other custom-fit in-ear EEG research (Kidmose et al, 2013; Mikkelsen et al, 2015). The fitting and manufacturing of custom-fit earpieces for each recruited participant was the main limitation to increasing our sample size.…”

Section: Discussion Limitations and Directions For Future Worksupporting

confidence: 74%

“…In 2005, Thorpe et al motivated and outlined the design of a passthoughts system (Thorpe et al, 2005). Since 2002, a number of independent groups have achieved 99–100% authentication accuracy for small populations using research-grade and consumer-grade scalp-based EEG systems (Poulos et al, 2002; Marcel and Millan, 2007; Ashby et al, 2011; Chuang et al, 2013). Several recent works on brainwave biometrics have independently demonstrated individuals' EEG permanence over 1–6 months (Armstrong et al, 2015; Maiorana et al, 2016) or even over 1 year (Ruiz-Blondet et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

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One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

et al. 2019

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In-ear EEG offers a promising path toward usable, discreet brain-computer interfaces (BCIs) for both healthy individuals and persons with disabilities. To test the promise of this modality, we produced a brain-based authentication system using custom-fit EEG earpieces. In a sample of N = 7 participants, we demonstrated that our system has high accuracy, higher than prior work using non-custom earpieces. We demonstrated that both inherence and knowledge factors contribute to authentication accuracy, and performed a simulated attack to show our system's robustness against impersonation. From an authentication standpoint, our system provides three factors of authentication in a single step. From a usability standpoint, our system does not require a cumbersome, head-worn device.

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Section: Discussion Limitations and Directions For Future Worksupporting

confidence: 74%

Section: Related Workmentioning

confidence: 99%

One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

et al. 2019

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“…Relevance for individually personal information in EEG signals was revealed in the early 1930s [1]. Even though EEG has low spatial resolution compared to fMRI, several studies have shown considerable accuracy in this domain [2][3][4]. Pozo-Banos et al [5] reported in a comprehensive review about EEG subject identification that EEG has subject-specific information.…”

Section: Related Researchmentioning

confidence: 99%

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

2020

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Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using such technologies in real-world applications. In this research, the challenge has been addressed by reducing the complexity of the brain signal acquisition and analysis processes. This was achieved by reducing the number of electrodes, simplifying the critical task without compromising accuracy. Event-related potentials (ERP), a.k.a. time-locked stimulation, was used to collect data from each subject's head. Following a relaxation period, each subject was visually presented a random four-digit number and then asked to think of it for 10 seconds. Fifteen trials were conducted with each subject with relaxation and visual stimulation phases preceding each mental recall segment. We introduce a novel derived feature, dubbed Inter-Hemispheric Amplitude Ratio (IHAR), which expresses the ratio of amplitudes of laterally corresponding electrode pairs. The feature was extracted after expanding the training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to our results, perfect accuracy can also be achieved using fewer electrodes. However, AF3, AF4, F7, and F8 electrode combination with kNN classifier which yielded 99.0±0.8% testing accuracy is the best for person identification to maintain both user-friendliness and performance. Surprisingly, the relaxation phase manifested the highest accuracy of the three phases.

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“…A strong alpha wave is expressed as a low brain behavior index. Stable alpha behavior indicates high brain activity [40]. The beta wave, on the other hand, is associated with the state of thought, which is prominent in frontal cortex and the surrounding area.…”

Section: ) Eeg For Msamentioning

confidence: 99%

Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

et al. 2021

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Mood state assessment (MSA) is increasingly important for diagnosis and treatment of depression. Recent years, many approaches have been proposed for the process of MSA. When using a single approach for MSA, the user often has to deal with possible noisy data and unacceptable error rates. Novelty: In order to improve the accuracy of MSA, in this paper, we propose a novel high-level information fusion method for determining the MS of users by fusing physiological data, such as heart rate and brainwave information collected through a wearable device, and psychological data collected through a monthly mood chart. The multifaceted information must be collected and analyzed simultaneously. Contribution: In the inference process of proposed framework, we adopted a Bayesian Network (BN) to perform high-level information fusion. We exploited various evaluation approaches to evaluate the performance of the proposed approach. Result: We have conducted experiments using two datasets and evaluated the performance using various factors. The results show that the proposed method (7-M Bayesian Fusion) is superior to other methods averagely 9.48 % improvement in most evaluation factors. It reveals that the proposed approach is efficient in fusing the MS information required for accurate diagnosis of depression compared with those approaches without fusion approach or with few information fusing.

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Person Identification from the EEG using Nonlinear Signal Classification

Cited by 122 publications

References 28 publications

One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

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