Detection of Non-Stationary GW Signals in High Noise From Cohen’s Class of Time–Frequency Representations Using Deep Learning

Lopac, Nikola; Hržić, Franko; Vuksanović, Irena Petrijevčanin; Lerga, Jonatan

doi:10.1109/access.2021.3139850

Cited by 56 publications

(43 citation statements)

References 91 publications

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“…The paper [52] proposed an approach to the problem of classification of noisy nonstationary time series signals based on the class of their Cohen time-frequency representations (TFR) and deep learning algorithms. The authors of the research presented an example of detecting gravitational wave (GW) signals in intense, real, nonstationary, nonwhite and non-Gaussian noise.…”

Section: Related Workmentioning

confidence: 99%

“…[36,37] LORETA, nervous tics, EEG, biomedical signals, eliminate biological artifacts[38,39] Electro-oculographic artifacts, nervous tics, EEG, biomedical signals, power spectrum analysis, BCI, insomnia disorder[40] Artifact removal algorithm, nervous tics, EEG, biomedical signals, BCI hybrid system[41] Motor imaging, BCI system, IC MARC classifier, EEG, muscle artifacts[42] Automatic removal of artifacts, OD-ICA, EEG, ocular artifact[43] BCI system, peak detection, EEG, peak frequency detection[44] Denoising autoencoder, Lomb-Scargle periodogram, EEG[45][46][47] Convolutional neural network, skin cancer classification system, deep neural network, classification of breast cancer histopathological images[48][49][50] Artificial intelligence in the regulation of automation systems, classification system[51][52][53] Artificial intelligence, EEG, spectrogram, convolutional neural networks, classification system…”

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confidence: 99%

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BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

Pawuś

Paszkiel

2022

Applied Sciences

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The constantly developing biomedical engineering field and newer and more advanced BCI (brain–computer interface) systems require their designers to constantly develop and search for various innovative methods used in their creation. In response to practical requirements and the possibility of using the system in real conditions, the authors propose an advanced solution using EEG (electroencephalography) signal analysis. A BCI system design approach using artificial intelligence for the advanced analysis of signals containing facial expressions as control commands was used. The signals were burdened with numerous artifacts caused by simulated nervous tics. The proposed expert system consisted of two neural networks. The first one allowed for the analysis of one-second samples of EEG signals from selected electrodes on the basis of power spectrum estimation waveforms. Thus, it was possible to generate an appropriate control signal as a result of appropriate facial expression commands. The second of the neural networks detected the appearance and type of nervous tics in the signal. Additionally, the participants were affected by interference such as street and TV or radio sound, Wi-Fi and radio waves. The system designed in such a way is adapted to the requirements of the everyday life of people with disabilities, in particular those in wheelchairs, whose control is based on BCI technology.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

Pawuś

Paszkiel

2022

Applied Sciences

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“…The experimental results show that MGANet is superior to the most advanced baseline. In future work, it could be expanded to emotion recognition, speech processing, non-stationary signal analysis, and other fields [ 28 , 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Wang

Guo

Shen

et al. 2022

Sensors

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Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to extract the most representative features according to confused transitional information in confused stages. (3) How to improve classification accuracy of sleep stages compared with existing models. To address these shortcomings, we propose a multi-layer graph attention network (MGANet). Node-level attention prompts the graph attention convolution and GRU to focus on and differentiate the interaction between channels in the time-frequency domain and the spatial domain, respectively. The multi-head spatial-temporal mechanism balances the channel weights and dynamically adjusts channel features, and a multi-layer graph attention network accurately expresses the spatial sleep information. Moreover, stage-level attention is applied to easily confused sleep stages, which effectively improves the limitations of a graph convolutional network in large-scale graph sleep stages. The experimental results demonstrated classification accuracy; MF1 and Kappa reached 0.825, 0.814, and 0.775 and 0.873, 0.801, and 0.827 for the ISRUC and SHHS datasets, respectively, which showed that MGANet outperformed the state-of-the-art baselines.

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“…Zhang et al [55] proposed a new method more suitable for farmland vacancy segmentation, using the improved RESNET network as the backbone of signal transmission. Lopac et al [56] proposed a method for the classification of noisy non-stationary time-series signals based on Cohen's class of their time-frequency representations (TFRs) and deep learning algorithms. The proposed approach combining deep CNN architectures with Cohen's class TFRs yields high values of performance metrics and significantly improves the classification performance compared to the base model.…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

A Survey of Multi-Focus Image Fusion Methods

Zhou

Zhi

et al. 2022

Applied Sciences

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As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.

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Detection of Non-Stationary GW Signals in High Noise From Cohen’s Class of Time–Frequency Representations Using Deep Learning

Abstract: This work was supported by the Croatian Science Foundation under the project IP-2018-01-3739, IRI2 project "ABsistemDCiCloud" (KK.01.2.1.02.0179), the University of Rijeka under the projects uniri-tehnic-18-17 and uniri-tehnic-18-15, and the European COST project CA17137.

Cited by 56 publications

References 91 publications

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

A Survey of Multi-Focus Image Fusion Methods

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