Task sensitivity in EEG biometric recognition

Yang, Su; Deravi, Farzin; Hoque, Sanaul

doi:10.1007/s10044-016-0569-4

Cited by 51 publications

(72 citation statements)

References 35 publications

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“…Three types of DWT were proposed to use in previous works: Haar Wavelet Transform which is described by (4) and (5) [18]. Daubechies Wavelet Transform (db4) which is described by (6) and (7) [19], and Bi-orthogonal (Tap9/7) Transform which is described by (8)(9)(10)(11)(12)(13) [20]:…”

Section: B) Statistical Moments Featuresmentioning

confidence: 99%

“…Yang et al [10] discussed the sensitivity of EEG-based recognition system to the type of mental tasks; they proposed Daubechies (db4) packet decomposition and calculated the standard deviation of each sub-band as features. Features from different tasks and electrodes (9 electrodes) were fused to generate the final features vector, then they fed to Linear Discriminant Analysis (LDA) classifier to classify (108) subjects from MMI dataset.…”

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confidence: 99%

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User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

George¹,

Hadi²

2019

IJIMAI

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Section: B) Statistical Moments Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

George¹,

Hadi²

2019

IJIMAI

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“…The EEG-MMI benchmark has been used to research the effects of movement-related tasks on biometric recognition. 40 The 4-sec long task-related recordings were joined into 30-sec long epochs in order to extract the biometric features by using a wavelet transform. The recognition was performed with linear discriminant analysis, and an accuracy of nearly to 100% was achieved.…”

Section: Spectral-based Featuresmentioning

confidence: 99%

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

Schetinin

Jakaite

Nyah

et al. 2018

Int. J. Neur. Syst.

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The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique "brain print", which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

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“…CNN is a machine learning method which is inspired from the biological system [30], which was originally proposed for image classification task [31]. Due to its great potential in analysis of small details presented by pixels in an image, CNN is also applicable for EEG analysis [32][33][34]. is is because the data points of the EEG can be arranged in matrix form, which is similar to the matrix of pixels [35].…”

Section: Introductionmentioning

confidence: 99%

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Lai

Ibrahim

Hamid

et al. 2020

Computational Intelligence and Neuroscience

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Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

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Task sensitivity in EEG biometric recognition

Cited by 51 publications

References 35 publications

User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

User Identification and Verification from a Pair of Simultaneous EEG Channels Using Transform Based Features

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

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