Sparse EEG compressive sensing for web-enabled person identification

Dai, Yixiang; Wang, Xue; Li, Xuanping; Tan, Yuqi

doi:10.1016/j.measurement.2015.07.008

Cited by 42 publications

(15 citation statements)

References 27 publications

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“…Brain biometric systems typically contain two parts, both for authentication [71] and identification applications [30]. These are (i) the data acquisition part and (ii) the decision part.…”

Section: Brain Biometric Recognition Systemmentioning

confidence: 99%

“…Furthermore, this could result in a significant challenge in computational complexity, especially for real-time recognition. Nowadays, EEG sensing is more likely to use wearable 112:15 devices for raw data acquisition and wireless data transmission, even for web-based applications [30]. These could lead to another limitation in terms of data transmission rate.…”

Section: Channel/feature Selection and Dimensionality Reductionmentioning

confidence: 99%

“…Nonlinear kernels are introduced to map data to another space to make them more separable while increasing the classifier's complexity. One frequently used kernel is the Radial Basis Function (RBF) [30,37,89,106,111,113,127,182]. Other kernels, like sigmoid and polynomial, were also tested [106].…”

Section: Support Vector Machinementioning

confidence: 99%

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A Survey on Brain Biometrics

et al. 2019

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Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability. CCS Concepts: • Security and privacy → Biometrics; • Computing methodologies → Machine learning approaches; • Applied computing → Psychology;

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“…Brain biometric systems typically contain two parts, both for authentication [71] and identification applications [30]. These are (i) the data acquisition part and (ii) the decision part.…”

Section: Brain Biometric Recognition Systemmentioning

confidence: 99%

Section: Channel/feature Selection and Dimensionality Reductionmentioning

confidence: 99%

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A Survey on Brain Biometrics

et al. 2019

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“…It owns the benefits of low cost, easy usability, and high temporal resolution. For example, EEG recordings are important for the description of the irritant and ictal onset zones in the presurgical evaluation of refractory partial epilepsy [1]; motor imagery EEG signals provide an important basis for designing a way to communicate between the brain and computer [2]; by making use of sparse EEG compressive sensing, person identification is possible [3]; and EEG can be utilized with other physiological data of different types to make a study of brain functions [4]. Nevertheless, with relatively low amplitudes, EEG is often polluted by many kinds of nonbrain artifacts mainly from the electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) interferences.…”

Section: Introductionmentioning

confidence: 99%

Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis

Liu

Zhang

et al. 2019

Journal of Healthcare Engineering

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Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

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“…EEG is one of the most important physiological signals to analyze mental workload. It reflects the electrical activity of the cortex directly [ 1 ]. EEG has high temporal resolution, which is important to measure mental states continuously.…”

Section: Introductionmentioning

confidence: 99%

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload

Wang

Chen

You

2017

Sensors

Self Cite

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Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

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Sparse EEG compressive sensing for web-enabled person identification

Cited by 42 publications

References 27 publications

A Survey on Brain Biometrics

A Survey on Brain Biometrics

Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload

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