Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques

Kumar, Mari Ganesh; Narayanan, Shrikanth; Sur, Mriganka; Murthy, Hema A.

doi:10.1109/tifs.2021.3067998

Cited by 15 publications

(19 citation statements)

References 44 publications

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“…Hence, the success of the single-channel SSVEP-based biometric approach for 11 people may have been dependent on this implemented task. By using 12 and 15 Hz in our approach, the suggested frequency range (12)(13)(14)(15)(16)(17)(18) to provide maximum accuracy for BCI applications was included [40]. Furthermore, multi-channel recordings were also addressed and found to be helpful in capturing the high flickering frequencies as SSVEP responses [40].…”

Section: Bidirectional Models and Gated Recurrent Unitmentioning

confidence: 99%

“…Hence, these features can make the SSVEP a highly potential candidate for structuring real-time usage in human-computer interfaces consisting of biometric applications [10,11]. A great number of EEG-based and VEP-based biometric studies dealing with multi-trial and multi-channel recordings can be found in the literature [13][14][15][16][17][18][19]. However, few studies are based on the single-channel dry-electrode SSVEP approach with single-trial person identification [9,20].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Bidirectional Models and Gated Recurrent Unitmentioning

confidence: 99%

Section: Introductionmentioning

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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“…However, despite the fact that the number of electrodes has been effectively reduced, the application in reengineering remains inconvenient. In 2021, Mari Ganesh Kumar et al [30] proposed the best subspace approach (ix-vector) to identify persons with an accuracy of 86.4 % with only 9 EEG channels. In 2022, Chiqin Lai [31]proposed a CNN combined with an error correcting output code of support vector machine with majority voting set (CNN-ECOC-SVM) for biometric recognition showing that 98.49 % accuracy was achieved in the proposed architecture.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…The electroencephalogram is produced by inducing an electric field on the subject's scalp, the characteristics of which are determined by the firing of spatially arranged cortical pyramidal neurons, and this brain activity is typically classified into five distinct oscillatory rhythms [5], namely delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (≥ 30 Hz). The characteristics of EEG signals are unique and enduring aspects of the brain, and compared to conventional biometric identification, EEG signals offer more visible benefits [6]: (i) High concealment: EEG signals are generated within the brain, necessitating the need for a specialized acquisition instrument; (ii) Liveness detection: creation of EEG signals must ensure owner activity in the area where they are placed, and EEG signals will vanish when the owner dies; (iii) Difficult to harm [7]: Compared to fingerprints, faces, and other biometric features that are exposed to the outside world for an extended period of time, the EEG is contained within the head and is the most vital and well-protected organ, which ensures that the EEG signal can be used as a biometric authentication without fear of causing damage to the EEG or allowing it to be identified.…”

Section: Introductionmentioning

confidence: 99%

EEG authentication based on deep learning of triplet loss

Cui¹,

Su²,

Wei³

et al. 2022

NNW

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As a novel biometric characteristic, the electroencephalogram (EEG) is used for biometric authentication. To solve the challenge of efficiently growing the number of classifications in traditional classification networks and to increase the practicality of engineering, this paper proposes an authentication approach for EEG data based on an attention mechanism and a triplet loss function. The method begins by feeding EEG signals into a deep convolutional network, maps them to 512-dimensional Euclidean space using a long short-term memory network combined with an attention mechanism, and obtains feature vectors for EEG signals with identity information; it then adjusts the network parameters using a triplet loss function, such that the Euclidean distance between feature vectors of similar signals decreases while the distance between signals of a different type increases. Finally, the recognition method is evaluated using publicly available EEG data sets. The experimental results suggest that the method maintains the recognition rate while effectively expanding the classifications of the model, hence thus boosting the practicability of EEG authentication.

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“…Projection of high-dimensional data into a lower-dimensional space is proposed in the literature to overcome the large data size problem [59]. Kumar et al [61] proposed a new approach for modelling multi-channel EEG data as biometric signatures regardless of task/condition by using the fundamentals of subspace-based text independent speaker verification. They applied the high dimensional statistics EEG signals, then projected them into a lower dimensional subspace.…”

Section: Introductionmentioning

confidence: 99%

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

Esener,

Kılınç,

Urazel

et al. 2023

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Advancements in EEG biometric technologies have been hindered by two persistent challenges: the management of large data sizes and the unreliability of data resulting from various measurement environments. Addressing these challenges, this study introduces a novel methodology termed 'Cube-Code' for cognitive biometric authentication. As a preliminary step, Automatic Artifact Removal (AAR) leveraging wavelet Independent Component Analysis (wICA) is applied to EEG signals. This step transforms the signals into independent sub-components, effectively eliminating the effects of muscle movements and eye blinking. Subsequently, unique 3-Dimensional (3-D) Cube-Codes are generated, each representing an individual subject in the database. Each Cube-Code is constructed by stacking the alpha, beta, and theta sub-band partitions, obtained from each channel during each task, back-to-back. This forms a third-order tensor. The stacking of these three subbands within a Cube-Code not only prevents a dimension increase through concatenation but also permits the direct utilization of non-stationary data, bypassing the need for fiducial component detection. Higher-Order Singular Value Decomposition (HOSVD) is then applied to perform a subspace analysis on each Cube-Code, an approach supported by previous literature concerning its effectiveness on 3-D tensors. Upon completion of the decomposition process, a flattening operation is executed to extract lower-dimensional, taskindependent feature matrices for each subject. These feature matrices are then employed in five distinct deep learning architectures. The Cube-Code methodology was tested on EEG signals, composed of different tasks, from the PhysioNet EEG Motor Movement/Imagery (EEGMMI) dataset. The results demonstrate an authentication accuracy rate of approximately 98%. In conclusion, the novel Cube-Code methodology provides highly accurate results for subject recognition, delivering a new level of reliability in EEG-based biometric authentication.

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Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques

Cited by 15 publications

References 44 publications

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

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

EEG authentication based on deep learning of triplet loss

High-Dimension EEG Biometric Authentication Leveraging Sub-Band Cube-Code Representation

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