Identity Authentication Using Portable Electroencephalography Signals in Resting States

RongKai, Zhang; Yan, Bin; Li, Tong; Shu, Jun; Song, Xiaoxiao; Zeng, Ying

doi:10.1109/access.2019.2950366

Cited by 11 publications

(10 citation statements)

References 49 publications

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“…In addition, two studies explored how on-body wearable device placement affects reidentification. Noh et al 25 found that the bioimpedance CIR was higher at the wrist (95•7%) than at the finger (77•6%), while Zhang et al 26 found the ECG CIR to be higher using measurements from a single arm (98•8%) as compared with using electrodes next to each ear (91•1%). We explored the biometric identification performance of studies with the highest number of subjects for each sensing modality (appendix p 2).…”

Section: Resultsmentioning

confidence: 99%

“…30 The system that could enable re-identification with the least amount of data was the Neurosky Mindwave 1-electrode EEG, which was used during resting with eyes open (N=46) and achieved a CIR of 95·48% with just two seconds of recorded data. 26…”

Section: Resultsmentioning

confidence: 99%

“…24 We analyzed the on-body positioning of all wearables used (Figure 4). The majority of the devices were positioned on the wrist (26), head (16), or chest (13), and some of the sensing modalities were tested on multiple on-body locations (e.g., ECG was measured using sensors behind the ear, on the upper arm, on the chest, or on the wrist). In addition, two studies explored how on-body wearable device placement affects reidentification.…”

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confidence: 99%

See 2 more Smart Citations

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Chikwetu

Woldetensae

et al. 2022

Preprint

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It remains unknown whether de-identifying wearable biometric monitoring data is sufficient to protect the privacy of individuals in the dataset. This systematic review seeks to shed light on this. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on December 6, 2021 (PROSPERO CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Though our search strategy had no language restrictions, all retrieved studies were in English. We included studies demonstrating re-identification, identification, or authentication using data from wearables. Our search returned 17,625 studies, and 72 studies met our inclusion criteria. Our findings demonstrate that substantial re-identification risk exists in data from sensors generally not thought to generate identifiable information, such as the electrocardiogram and electromyogram. In many cases, only a small amount of data (1-300 seconds of recording) is sufficient for re-identification.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Figure 2 Prisma Diagram Illustrating the Article Selection P...mentioning

confidence: 99%

See 1 more Smart Citation

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Chikwetu

Woldetensae

et al. 2022

Preprint

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show abstract

“…For example, fingerprints, iris, and facial recognition all display unique attributes of an individual [ 5 ]. In recent studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] electroencephalographic (EEG) signals were tested for their use in this biometric domain. It was postulated that EEG can serve as a biometric method for person authentication due to the unique pathways developed by each individual to accomplish a given mental activity [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Ketola

Barankovich

Schuckers

et al. 2022

Sensors

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Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32–64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks.

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“…Gui et al, [8] used Euclidean and Dynamic Time Warping (DTW) based matching technique and achieved an accuracy of 80% and 68% respectively. Zhang et al, [9] used single-channel EEG to identify 46 subjects using Rayleigh quotient feature selection and ensemble classifier with an accuracy of 95.48%. Yang et al, [10] proposed a method that utilizes the logarithm of wavelet coefficients and discrete cosine transform.…”

Section: Introductionmentioning

confidence: 99%

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

et al. 2021

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Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this paper. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experimentspersonalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for Random Forest classifier using 5 fold cross-validation. These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 6X faster system with 93.64% accuracy. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.

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Identity Authentication Using Portable Electroencephalography Signals in Resting States

Cited by 11 publications

References 49 publications

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Does de-identification of data from wearable Biometric Monitoring Technologies give us a false sense of security? A systematic review

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

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