Individual Identification Using Cognitive Electroencephalographic Neurodynamics

Min, Byoung-Kyong; Suk, Heung-Il; Ahn, Min-Hee; Lee, Seong–Whan

doi:10.1109/tifs.2017.2699944

Cited by 32 publications

(10 citation statements)

References 51 publications

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“…Motor tasks (eye closing [13], hands movement [14], etc. ), visual stimulation [15]- [17] and multiple mental tasks such as mathematical calculation, writing text, and imagining movements ( [18]) are three major tasks in stimulating brain responses for EEGbased PI [19]. To identify a person, it is very important to investigate the stimulating tasks which can induce personal brain response patterns.…”

Section: Introductionmentioning

confidence: 99%

Affective EEG-Based Person Identification Using the Deep Learning Approach

Wilaiprasitporn

Ditthapron

Matchaparn

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

142

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Electroencephalography (EEG) is another mode for performing Person Identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while the person is performing some kind of mental task, such as motor control. However, few works have considered EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEGbased PI using a deep learning approach. We proposed a cascade of deep learning using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. We evaluated two types of RNNs, namely, Long Short-Term Memory (CNN-LSTM) and Gated Recurrent Unit (CNN-GRU). The proposed method is evaluated on the state-of-the-art affective dataset DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90-100% mean Correct Recognition Rate (CRR), significantly outperforming a support vector machine (SVM) baseline system that uses power spectral density (PSD) features. Notably, the 100% mean CRR comes from only 40 subjects in DEAP dataset. To reduce the number of EEG electrodes from thirty-two to five for more practical applications, the frontal region gives the best results reaching up to 99.17% CRR (from CNN-GRU). Amongst the two deep learning models, we find CNN-GRU to slightly outperform CNN-LSTM, while having faster training time. Furthermore, CNN-GRU overcomes the influence of affective states in EEG-Based PI reported in the previous works.

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Section: Introductionmentioning

confidence: 99%

Affective EEG-Based Person Identification Using the Deep Learning Approach

Wilaiprasitporn

Ditthapron

Matchaparn

et al. 2020

IEEE Trans. Cogn. Dev. Syst.

142

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show abstract

“…On the other hand, the bivariate features are based on brain connectivity which captures the interactive or structural information between EEG channels. Different statistical and effective metrics have been used for establishing connectivity between EEG channels, including the Pearson's correlation [4], [17], Granger causality [18], spectral coherence [7], and phase synchronization indices [17], [10]. Moreover, graph features extracted from the brain connectivity networks are also proposed for EEG biometrics [19], [10].…”

Section: A Eeg Biometricsmentioning

confidence: 99%

Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems

Wang¹,

Wang²,

Hu³

2022

Preprint

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As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as fingerprints and face recognition, research on EEG biometrics is still in its infant stage. Most of the studies focus on either designing signal elicitation protocols from the perspective of neuroscience or developing feature extraction and classification algorithms from the viewpoint of machine learning. These studies have laid the ground for the feasibility of using EEG as a biometric authentication modality, but they have also raised security and privacy concerns as EEG data contains sensitive information. Existing research has used hash functions and cryptographic schemes to protect EEG data, but they do not provide functions for revoking compromised templates as in cancellable template design. This paper proposes the first cancellable EEG template design for privacy-preserving EEG-based authentication systems, which can protect raw EEG signals containing sensitive privacy information (e.g., identity, health and cognitive status). A novel cancellable EEG template is developed based on EEG graph features and a non-invertible transform. The proposed transformation provides cancellable templates, while taking advantage of EEG elicitation protocol fusion to enhance biometric performance. The proposed authentication system offers equivalent authentication performance (8.58% EER on a public database) as in the nontransformed domain, while protecting raw EEG data. Furthermore, we analyze the system's capacity for resisting multiple attacks, and discuss some overlooked but critical issues and possible pitfalls involving hill-climbing attacks, second attacks, and classification-based authentication systems.

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“…Row segments (5, 6, and 7 Hz) and column segments (5.5, 6.5, and 7.5 Hz) were induced in a grid-shaped line array to construct a Korean letter. According to the study, 20 persons were classified using Support Vector Machine (SVM) with an accuracy score of 98.60% [21]. In another study, four flickering frequencies (6,12,18,and 24 Hz) were implemented to stimulate brain responses via nine green light-emitting diodes (LEDs).…”

Section: Min Et Al Studied Ssvep-based Identification Using the Neuro...mentioning

confidence: 99%

The Single-Channel SSVEP-based Biometric Approach Using RNN-based Deep Models: A Systematic Comparison

Gorur

Eraslan

2022

Preprint

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Biometric technology is the branch of science dealing with identification and verification of individuals, focusing on physiological and behavioral traits. These traits can be reliable, permanent, unique, and capable of distinguishing one person from others. Fingerprints, the iris, and the face are conventionally used in biometric techniques involving ID cards and passwords. However, due to security threats and fraud, these static textures and approaches can be abused. Consequently, biometric studies based on electroencephalography (EEG) have received increasing attention because each individual has a dynamic and unique pattern. However, classic EEG-based biometrics have significant deficiencies, including noise-prone signals, gel-based electrodes, and the need for multi-training/multi-channel acquisition and high mental effort. In contrast, steady-state visually evoked potential (SSVEP)-based biometrics have the important advantages of low signal-to-noise ratio and untrained usage. The SSVEP signals also embrace a number of other advantages. First, the occipital lobe is the only suitable region for collecting SSVEP signals without obstruction. Second, because elicited dynamic brain responses are a natural subconscious activity, the omnipresent SSVEP signals are the most frequently emitted signals. Anyone can continuously look into flickering lights having distinct frequencies, such as cell phone flashes, without extra physical or mental effort. Few studies involving multi-channel/multi-trial SSVEP-based biometric research are available in the current literature. Moreover, there is a lack of research comparing them to the single-channel single-trial SSVEP-based biometric approach using dry electrode-implemented recurrent neural network (RNN)-based deep learning models. To the best of our knowledge, no prior work has proposed such a biometric comparison of the RNN-based deep learning models and different sized time-series data compiled over raw and discrete wavelet transform (DWT)-based SSVEP signals. By achieving up to 100% accuracy using 11 individuals, the biometric recognition results are promising. This single-channel SSVEP-based biometric approach using RNN-based deep learning models may offer low-cost, user-friendly, and reliable individual identification authentication, leading to significant application domains.

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Individual Identification Using Cognitive Electroencephalographic Neurodynamics

Cited by 32 publications

References 51 publications

Affective EEG-Based Person Identification Using the Deep Learning Approach

Affective EEG-Based Person Identification Using the Deep Learning Approach

Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems

The Single-Channel SSVEP-based Biometric Approach Using RNN-based Deep Models: A Systematic Comparison

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