Artykuły w czasopismach: „EOG”

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Reilly, Richard B., i T. Clive Lee. "Electrograms (ECG, EEG, EMG, EOG)". Technology and Health Care 18, nr 6 (19.11.2010): 443–58. http://dx.doi.org/10.3233/thc-2010-0604.

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Webster, John G. "Biomedical Instrumentation". International Journal of Systems Biology and Biomedical Technologies 3, nr 1 (styczeń 2015): 20–38. http://dx.doi.org/10.4018/ijsbbt.2015010102.

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This paper covers the measurement of biopotentials for diagnosis: the electrical voltages that can be measured from electrodes placed on the skin or within the body. Biopotentials include: the electrocardiogram (ECG), electroencephalogram (EEG), electrocortogram (ECoG), electromyogram (EMG), electroneurogram (ENG), electrogastrogram (EGG), action potential (AP), electroretinogram (ERG), electro-oculogram (EOG). This paper also covers skin conductance, pulse oximeters, urology, wearable systems and important therapeutic devices such as: the artificial cardiac pacemaker, defibrillator, cochlear implant, hemodialysis, lithotripsy, ventilator, anesthesia machine, heart-lung machine, infant incubator, infusion pumps, electrosurgery, tissue ablation, and medical imaging. It concludes by covering electrical safety. It provides future subjects for research such as a blood glucose sensor and a permanently implanted intracranial pressure sensor.

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Siting, Zhao, Kishan Kishan i Amiya Patanaik. "271 Sleep staging performance of a signal-agnostic cloud-based real-time sleep analytics platform". Sleep 44, Supplement_2 (1.05.2021): A108—A109. http://dx.doi.org/10.1093/sleep/zsab072.270.

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Abstract Introduction The coronavirus pandemic has brought unprecedented changes to the health care system, including sleep medicine. Remote monitoring and telemedicine played a significant role in this shift. We anticipate these changes to continue in the future with internet-connected wearables (ICWs) playing an important role in measuring and managing sleep remotely. As these ICWs measures a small subset of signals traditionally measured during polysomnography (PSG), manual sleep staging becomes non-trivial and sometimes impossible. The ability to do accurate and reliable automatic sleep staging using different modalities of physiological signals remotely is becoming ever more important. Methods The current work seeks to quantify the sleep staging performance of Z3Score-Neo (https://z3score.com, Neurobit Technologies, Singapore), a signal agnostic, cloud-based real-time sleep analytics platform. We tested its staging performance on the CINC open dataset with N=994 subjects using various combinations of signals including Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), and Instantaneous Heart Rate (IHR) derived from Electrocardiogram (ECG). The staging was compared against manual scoring based on PSG. For IHR based staging, N1 and N2 were combined. Results We achieved substantial agreement (all Cohen’s Kappa > 0.7) between automatic and manual staging using various combinations of EEG, EOG and EMG channels with accuracies varying between 81.76% (two central EEGs, one EOG, one EMG), 79.31% (EEG+EOG), 78.73% (EEG only) and 78.09% (one EOG). We achieved moderate agreement (accuracy: 72.8% κ=0.54) with IHR derived from ECG. Conclusion Our results demonstrated the accuracy of a cloud-based sleep analytics platform on an open dataset, using various combinations of ecologically valid physiological signals. EOG and EMG channels can be easily self-administered using sticker-based electrodes and can be added to existing home sleep apnea test (HSAT) kits significantly improving their utility. ICWs are already capable of accurately measuring EEG/EOG (Muse, InteraXon Inc., Toronto, Canada; Dreem band, Dreem, USA) and IHR derived from ECG (Movesense, Suunto, Finland) or photoplethysmogram (Oura Ring, Oura Health Oy, Finland) or through non-contact ballistocardiogram/radio-based measurements (Dozee, Turtle Shell Technologies, India; Sleepiz, Sleepiz AG, Switzerland). Therefore, a well-validated cloud-based staging platform solves a major technological hurdle towards the proliferation of remote monitoring and telehealth in sleep medicine. Support (if any):

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Hossain, Md Shafayet, Sakib Mahmud, Amith Khandakar, Nasser Al-Emadi, Farhana Ahmed Chowdhury, Zaid Bin Mahbub, Mamun Bin Ibne Reaz i Muhammad E. H. Chowdhury. "MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals". Bioengineering 10, nr 5 (10.05.2023): 579. http://dx.doi.org/10.3390/bioengineering10050579.

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Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models’ performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.

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Martinek, Radek, Martina Ladrova, Michaela Sidikova, Rene Jaros, Khosrow Behbehani, Radana Kahankova i Aleksandra Kawala-Sterniuk. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals". Sensors 21, nr 18 (10.09.2021): 6064. http://dx.doi.org/10.3390/s21186064.

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Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

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D'Souza, Sandra, i N. Sriraam. "Design of EOG Signal Acquisition System Using Virtual Instrumentation". International Journal of Measurement Technologies and Instrumentation Engineering 4, nr 1 (styczeń 2014): 1–16. http://dx.doi.org/10.4018/ijmtie.2014010101.

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The design and development of cost effective rehabilitation aids is a challenging task for biomedical research community. The biopotentials such as EEG, EMG, ECG and EOG that are generated from human body help in controlling the external electronic devices. In the recent years, the EOG based assistive devices have gained importance in assisting paralyzed patients, due to their ability to perform operations controlled by retinal movements. This paper proposes a cost effective design and development of EOG signal acquisition system using virtual instrumentation. The hardware design comprises of two instrumentation amplifiers using AD620 for registering horizontal and vertical eye movements and filter circuits. A virtual instrumentation based front panel is designed to interface the hardware and to display the EOG signals. The resultant digitized EOG signal is further enhanced for driving assistive devices. The proposed EOG system makes use of virtual instrumentation and hence minimizes the design cost and increases the flexibility of the instrument. This paper presents the initial part of the research work which is aiming at a cost effective complete assistive device based on extracting the useful information from the eye movements. The qualitative validation of EOG signals recorded ensures the cost effective healthcare delivery for rehabilitation applications.

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Zhu, Hangyu, Cong Fu, Feng Shu, Huan Yu, Chen Chen i Wei Chen. "The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods". Bioengineering 10, nr 5 (10.05.2023): 573. http://dx.doi.org/10.3390/bioengineering10050573.

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The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.

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Antony, Mary Judith, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally i Rakesh Kumar Mahendran. "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data". Diagnostics 13, nr 17 (3.09.2023): 2852. http://dx.doi.org/10.3390/diagnostics13172852.

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An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL–SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method’s ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

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Tejedor, Javier, Constantino A. García, David G. Márquez, Rafael Raya i Abraham Otero. "Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review". Sensors 19, nr 21 (29.10.2019): 4708. http://dx.doi.org/10.3390/s19214708.

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This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

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ISHII, Chiharu. "Control of an Electric Wheelchair Based on EMG, EOG and EEG". Journal of the Japan Society for Precision Engineering 83, nr 11 (2017): 1006–9. http://dx.doi.org/10.2493/jjspe.83.1006.

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Tandle, Avinash, Nandini Jog, Pancham D'cunha i Monil Chheta. "Classification of Artefacts in EEG Signal Recordings and EOG Artefact Removal using EOG Subtraction". Communications on Applied Electronics 4, nr 1 (26.01.2016): 12–19. http://dx.doi.org/10.5120/cae2016651997.

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Klados, Manousos A., i Panagiotis D. Bamidis. "A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques". Data in Brief 8 (wrzesień 2016): 1004–6. http://dx.doi.org/10.1016/j.dib.2016.06.032.

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Riyadh Mahmood, Hassanein, Manaf K. Hussein i Riyadh A. Abedraba. "Development of Low-Cost Biosignal Acquisition System for ECG, EMG, and EOG". Wasit Journal of Engineering Sciences 10, nr 3 (1.12.2022): 191–202. http://dx.doi.org/10.31185/ejuow.vol10.iss3.352.

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The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB) wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.

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PERDHANA, HASBIAN FAUZY, i HASBALLAH ZAKARIA. "Pembersihan Artefak EOG dari Sinyal EEG menggunakan Denoising Autoencoder". ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, nr 3 (19.07.2022): 639. http://dx.doi.org/10.26760/elkomika.v10i3.639.

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ABSTRAKElektroensefalografi (EEG) adalah teknik perekaman yang merekam aktivitas elektrik pada otak menggunakan elektroda yang ditempelkan pada kulit kepala. Artefak elektrookulografi (EOG) adalah salah satu artefak yang kerap muncul pada perekaman EEG dikarenakan pergerakan mata dan menyebabkan sinyal EEG berubah bentuk. Untuk membersihkan EEG, artefak perlu dibuang dengan tetap menjaga informasi penting dari EEG. Pada penelitian ini kami mendeteksi artefak EOG menggunakan Independent Component Analysis (ICA) dan deteksi puncak, dan untuk rekonstruksi sinyal EEG kami menggunakan Denoising Autoencoder (DAE). Pada penelitian ini kami meneliti model DAE apakah dapat merekonstruksi sinyal EEG dari artefak EOG. Metode pendeteksian artefak mendapatkan 85% sensitivitas dan 83% Positive Predictive Value (PPV) pada dataset sekunder dan 82% sensitivitas pada dataset primer. Model DAE dilatih dengan validasi silang 10 lipat dan mendapatkan rerata mean squared error (MSE) 0,007±0,008. Penelitian ini membuktikan kemampuan DAE untuk merekonstruksi sinyal EEG denganmasukan segmen sinyal EEG terkontaminasi artefak EOG.Kata kunci: EEG, Artefak EOG, Denoising Autoencoder ABSTRACTThe Electroencephalography (EEG) is a recording technique to record electrical activity on the brain using electrodes attached to the head scalp. Electrooculography (EOG) is one of the artifacts that are prone to appear on EEG due to eye movement and cause EEG signals to deform. To fix the EEG signal, we need to remove artifacts while conserving EEG information. In this research, we detect EOG artifactual signal using Independent Component Analysis (ICA) and peak detection and used a generative model Denoising Autoencoder (DAE) to reconstruct clean EEG by using EEG artifact-corrupted signal. Our artifact detection method scores 85% sensitivity and 83% Positive Predictive Value on the secondary dataset and 82% sensitivity on the primary dataset. We train the DAE model with 10-fold cross-validation and got 0.007 ± 0.008 Mean Squared Error (MSE). We demonstrated DAE on its ability to generate a clean EEG segment by feeding it contaminated EEG segment.Keywords: EEG, Eye movement artifact, Denoising Autoencoder

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Garcia, Felipe, José Jair Alves Mendes Junior, Melissa La Banca Freitas i Sergio Luiz Stevan Jr. "Wearable Device for EMG and EOG acquisition". Journal of Applied Instrumentation and Control 6, nr 1 (28.02.2019): 30. http://dx.doi.org/10.3895/jaic.v6n1.8676.

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This article presents a wearable device capable of acquiring both facial EMG and EOG biopotentials. The circuit developed has amplification and filtering stages that eliminate higher frequency noises and low frequency biopotentials to use a band of frequency for acquisition of contractions from Frontalis muscle above the eyebrow and ocular movements on the vertical and horizontal axis. The output signal has an adjustment of gain and an offset that permits the application in embedded systems and digitally processing to eliminate 60 Hz noises. The wearable device is a mask with five electrodes allocated to make the acquisitions according to the positioning. The circuit is capable of acquiring both facial EMG and EOG simultaneously, but in this article they were acquired individually. Improvements in the whole system are being made and other ocular movements, like eye focus and involuntary movements for example, may be acquired in future works involving this system.

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Toma, Tabassum Islam, i Sunwoong Choi. "An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection". Sensors 23, nr 10 (21.05.2023): 4950. http://dx.doi.org/10.3390/s23104950.

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Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (ACC), Kappa (Kp), and F1 score (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of ACC, Kp, and F1 score are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.

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Sedik, Ahmed, Mohamed Marey i Hala Mostafa. "WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform". Applied Sciences 13, nr 5 (21.02.2023): 2785. http://dx.doi.org/10.3390/app13052785.

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As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) with machine learning and deep learning classifiers. The FFT and DWT are used for feature extraction and noise removal tasks. In addition, the classification task is carried out on the combined EEG, EOG, ECG, and EMG signals using machine learning and deep learning algorithms including 1D Convolutional Neural Networks (1D CNNs), Concatenated CNNs (C-CNNs), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k-Nearest Neighbor (KNN), Quadrature Data Analysis (QDA), Multi-layer Perceptron (MLP), and Logistic Regression (LR). The proposed methods are validated on two scenarios, multi-class and binary-class classification. The simulation results reveal that the proposed models achieved a high performance for fatigue detection from medical signals, with a detection accuracy of 90% and 96% for multiclass and binary-class scenarios, respectively. The works in the literature achieved a maximum accuracy of 95%. Therefore, the proposed methods outperform similar efforts in terms of detection accuracy.

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Nagpal, Chetna, i P. K. Uppadhyay. "Sleep EEG Classification Using Fuzzy Logic". International Journal of Advanced Research in Engineering 1, nr 1 (15.06.2015): 17. http://dx.doi.org/10.24178/ijare.2015.1.1.17.

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the computerized detection of multi stage system of EEG signals using fuzzy logic has been developed and tested on prerecorded data of the EEG of rats.The multistage detection system consists of three major stages: Awake, SWS (Slow wave sleep), REM (Rapid eye movement) which has been recorded and can be detected by the fuzzy classification and fuzzy rule base. The proposed work approaches to identify thestage of 3- channel signal on the basis of frequency distribution of EEG, standard deviation of EOG and EMG, variance of EOG and EMG. Based on feature extracted data, fuzzy logic rule base modelwas evaluated accurately in terms of 3 stages (Awake, SWS, and REM) and the result confirmed that the proposed model has potential in classifying the EEG signals

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BUCHWALD, Mikołaj. "Project and evaluation EMG/EOG human-computer interface". PRZEGLĄD ELEKTROTECHNICZNY 1, nr 7 (5.07.2017): 130–33. http://dx.doi.org/10.15199/48.2017.07.28.

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Sands, S. F., S. F. Faux i R. W. McCarley. "EOG artifact removal from EEG ERP topographic maps". Biological Psychiatry 25, nr 7 (kwiecień 1989): A28. http://dx.doi.org/10.1016/0006-3223(89)91542-4.

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Mannan, Malik M. Naeem, M. Ahmad Kamran, Shinil Kang i Myung Yung Jeong. "Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer Interface: A Comprehensive Study". Complexity 2018 (4.07.2018): 1–18. http://dx.doi.org/10.1155/2018/4853741.

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It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.

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Kaneko, Kenichi, i Kazuyoshi Sakamoto. "Spontaneous Blinks as a Criterion of Visual Fatigue during Prolonged Work on Visual Display Terminals". Perceptual and Motor Skills 92, nr 1 (luty 2001): 234–50. http://dx.doi.org/10.2466/pms.2001.92.1.234.

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Visual fatigue caused by prolonged work viewing a Visual Display Terminals (VDT) and of work reading a hard-copy were assessed by electromyogram (EMG) waveform and electrooculogram (EOG) waveform in spontaneous blinks as objective criteria, and by questionnaire of subjective feeling, and by task performance. The duration and the amplitude of the EMG of the orbicularis ocular muscle on the right side and the EOG of the vertical direction to the eyelid were measured for 10 subjects who participated in a figure task consisting of the addition of single-digit numbers on a VDT work or a work with a hard-copy. The mean values of the duration and the amplitude of the EMG and the EOG were evaluated by the averaging of 10 waveforms of the spontaneous blinks for all subjects. The time lag from the EMG to the EOG in the process of the generation of spontaneous blinks was also analyzed. These five parameters were evaluated during the work time. The mean values for the duration of the EMG increased gradually during the work time, but the amplitude did not show significant difference between the prework and a work time. There was no significant change of the duration of the EOG, but the mean amplitude of the EOG decreased as the work time progressed, and the time lag significantly extended. The blinks frequency increased relatively when using a VDT. The rate of fluctuation for these parameters was higher during use of a VDT than use of a hard-copy. The time lag at five hours of VDT work was extended by 90% based on the value at the prework. The symptoms of general fatigue and fatigue of the eyes increased linearly during the VDT work for six hours. The results indicated a significant correlation between the objective parameters for the activity of the spontaneous blinks, i.e., duration and amplitude of EMG and EOG, and the time lag between EMG and EOG, and the subjective feeling was recognized in the time course of the task. These experimental results suggested that the parameters regarding the EMG and the EOG for the spontaneous blinks were effective indices for assessing visual fatigue during prolonged VDT work.

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Issa, Mohamed F., i Zoltan Juhasz. "Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis". Brain Sciences 9, nr 12 (4.12.2019): 355. http://dx.doi.org/10.3390/brainsci9120355.

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Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.

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Malwatkar, Nupur Pranay. "ELECTRO-OCULOGRAPHY AS AN ALTERNATE COMMUNICATION IN THE FIELD OF HUMAN INTERFACING SYSTEM". International Journal of Engineering Applied Sciences and Technology 7, nr 7 (1.11.2022): 231–35. http://dx.doi.org/10.33564/ijeast.2022.v07i07.036.

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—In recent years several researchers are practicing on human interfacing techniques. Many techniques were successfully implemented and launched as commercial products for medical, aeronautics and bioinformatics communications. Still concerning electrooculography interfacing for Human Computer Interface (HCI) has wide scope of development and real life implementations. Like EEG, EMG and ECG, EOG doesn't provide important body parameters which could be used for disease diagnosis but it has very wide applications in human and machine interactions. EOG could be used by paralyzed stroke patients are unable to normally communicate with their environment. For these patients, the only part of their body that is under their control is eye moments. The system will detect the variations in electric signal strength through voltage level near the eye area and generates a signal in order to control interactive device. Additionally, they are particularly suitable in the case of people with severe motor disorder, for example people with other physical disorder. Developing solutions for them involves different ways of using sensors that decides the user’s needs and limitations, which in turn converts the user’s intentions into commands. This paper submits the use of EOG signals for the different application control in different fields in current situation

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Górecka, Joanna, i Andrzej Biedka. "Determination of Ocular Artifacts in the Clinical EEG Software by a Peripheral Device". Electronics 10, nr 2 (7.01.2021): 108. http://dx.doi.org/10.3390/electronics10020108.

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The occurrence of physiological artifacts generated by eye movements in electrical brain activity (electroencephalography, EEG) is a well-known problem in clinical practice. In order to increase the accuracy of the detection of eye movements during EEG examination, additional electrooculogram channels (electrooculography, EOG) with a standard PC keyboard are used. The EOG technique is not always comfortable for patients. Another issue is that the use of EOG channels in the EEG examination leads to the prolongation of time required for patient preparation. To solve these problems, we developed a new peripheral device suitable for the indication of common ocular artifacts in EEG. The obtained differences between the recommended methods (i.e., EOG, PC keyboard) and our new device have been presented using RMSE (root mean square error). The presented equipment can be used either during EEG examination or after registration of EEG signals in order to indicate the ocular artifacts. Furthermore, this device is compatible with the EEG software used in clinical practice.

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Górecka, Joanna, i Andrzej Biedka. "Determination of Ocular Artifacts in the Clinical EEG Software by a Peripheral Device". Electronics 10, nr 2 (7.01.2021): 108. http://dx.doi.org/10.3390/electronics10020108.

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The occurrence of physiological artifacts generated by eye movements in electrical brain activity (electroencephalography, EEG) is a well-known problem in clinical practice. In order to increase the accuracy of the detection of eye movements during EEG examination, additional electrooculogram channels (electrooculography, EOG) with a standard PC keyboard are used. The EOG technique is not always comfortable for patients. Another issue is that the use of EOG channels in the EEG examination leads to the prolongation of time required for patient preparation. To solve these problems, we developed a new peripheral device suitable for the indication of common ocular artifacts in EEG. The obtained differences between the recommended methods (i.e., EOG, PC keyboard) and our new device have been presented using RMSE (root mean square error). The presented equipment can be used either during EEG examination or after registration of EEG signals in order to indicate the ocular artifacts. Furthermore, this device is compatible with the EEG software used in clinical practice.

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Wang, Fuwang, Qing Xu, Rongrong Fu i Guangbin Sun. "Study of driving skill level discrimination based on human physiological signal characteristics". RSC Advances 8, nr 73 (2018): 42160–69. http://dx.doi.org/10.1039/c8ra08523d.

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Tagluk, M. Emin, Necmettin Sezgin i Mehmet Akin. "Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG". Journal of Medical Systems 34, nr 4 (8.04.2009): 717–25. http://dx.doi.org/10.1007/s10916-009-9286-5.

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Chandrasekaran, R., R. J. Hemalath, E. Anand Kumar, S. Murali, T. R. Thamizhvani i Soumya Y.K. "Spectral analysis of polysomnography". International Journal of Engineering & Technology 7, nr 2.25 (3.05.2018): 86. http://dx.doi.org/10.14419/ijet.v7i2.25.16565.

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The Polysomnography (PSG) is the most commonly used test in the diagnosis of OSAS – Obstructive Sleep Apnea Syndrome. PSG signals consist of simultaneous recording of multiple physiological parameters related to sleep and wakefulness. PSG is used to evaluate abnormalities of sleep and or wakefulness and other physiological disorders that have an impact on or related to sleep and or wakefulness. In this paper, we propped an idea of detection of insomnia based on frequency spectral analysis of PSG signals. The PSG signals consist of EMG of the chin, EEG taken from various lobes, respiratory signal, EOG signals, Temporary rectal signal and ECG signal. From all these physiological parameters, the Spectral analysis of EOG (horizontal), EEG FPZ-CZ and PZ-OZ [EEG 10-20 electrodes paced on midline FPZ,CZ,OZ channels]signals are analyzed and the mean, variance, standard deviation, RMS value and SNR features of the signal are extracted. The proposed methodology is applied to the male as well as female subjects at the age group of 30-40 years. The difference of the frequency range taken at respective intervals of time is noted and compared.

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K Firke, Omprakash, i Dr Manish Jain. "Analysis of Driver Drowsiness Detection using EEG and EOG". International Journal of Engineering & Technology 7, nr 2.17 (15.04.2018): 46. http://dx.doi.org/10.14419/ijet.v7i2.17.11557.

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This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.

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Torsvall, Lars, i Torbjörn Åkerstedt. "Extreme Sleepiness: Quantification of EOG and Spectral EEG Parameters". International Journal of Neuroscience 38, nr 3-4 (styczeń 1988): 435–41. http://dx.doi.org/10.3109/00207458808990704.

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Croft, Rodney J., i Robert J. Barry. "68 EOG correction of the EEG: Rethinking the problem". International Journal of Psychophysiology 30, nr 1-2 (wrzesień 1998): 28. http://dx.doi.org/10.1016/s0167-8760(98)90068-x.

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Croft, Rodney J., i Robert J. Barry. "674 EOG correction of the EEG: Rethinking the problem". International Journal of Psychophysiology 30, nr 1-2 (wrzesień 1998): 255. http://dx.doi.org/10.1016/s0167-8760(98)90673-0.

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Anas Fouad Ahmed. "A quick survey of EEG signal noise removal methods". Global Journal of Engineering and Technology Advances 11, nr 3 (30.06.2022): 098–104. http://dx.doi.org/10.30574/gjeta.2022.11.3.0100.

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An Electroencephalogram (EEG) is produced as a consequence of the electrical voltage of neurons in the brain. The EEG signal is crucial for detecting brain activity and attitude. Because this signal has very low amplitude, it is easily corrupted by different artefacts. The study and analysis of brain signals in the presence of these artifacts is a challenging task. ECG, EOG, EMG, and motion are the popular artifacts that induce disturbance to the EEG signal. This survey paper emphasizes the artifact elimination methods with their substantial parameters that must be considered during the study of published research on this trend.

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Yedukondalu, Jammisetty, i Lakhan Dev Sharma. "Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals". Sensors 23, nr 3 (21.01.2023): 1235. http://dx.doi.org/10.3390/s23031235.

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Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

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D’Souza, Sandra, i N. Sriraam. "Statistical Based Analysis of Electrooculogram (EOG) Signals". International Journal of Biomedical and Clinical Engineering 2, nr 1 (styczeń 2013): 12–25. http://dx.doi.org/10.4018/ijbce.2013010102.

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The use of Electrooculogram (EOG) signals for developing Human-Computer Interfaces is increasing in the recent times. Several advantages including ease and flexibility in acquiring EOG signals have encouraged insight into EOG based research. In order to identify optimal features for EOG signals for rehabilitation applications, it is necessary to apply the statistical basis to decide the selection of best feature. This paper suggests a pilot study on non-parametric statistical based approach for analyzing EOG signals. This paper considers the detailed statistical analysis of Electrooculogram (EOG) signals. The EOG signals are acquired by considering the horizontal and vertical movements of the eye. The recording includes subjects with identified age groups with different activities. Power spectral densities based on Welch, Yule-Walker, Burg methods are estimated from the acquired EOG signals. Then non-parametric based statistical analysis is performed to show whether the gender or age of the subject influences the EOG signal obtained for different activities. The experimental results based on statistical analysis show that the raw data did not hold any significance to categorize male-female or age wise grouping. However, some features extracted set from the raw data provides useful statistical information which will be of great importance when used for selective rehabilitation.

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Vidaurre, Carmen, Tilmann H. Sander i Alois Schlögl. "BioSig: The Free and Open Source Software Library for Biomedical Signal Processing". Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/935364.

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BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

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Amin, Abdullah Al. "A Feasibility Study of Employing EOG Signal in Combination with EEG Based BCI System for Improved Control of a Wheelchair". Bangladesh Journal of Medical Physics 10, nr 1 (3.12.2018): 47–58. http://dx.doi.org/10.3329/bjmp.v10i1.39150.

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For a fully paralysed person, EEG (Electroencephalogram) based Brain Computer Interface (BCI) has a great promise for controlling electromechanical equipment such as a wheelchair. Again EOG (Electrooculography) based Human Machine Interface system also provides a possibility. Individually, none of these methods is capable of giving a fully error free reliable and safe control, but an appropriate combination may provide a better reliability, which is the aim of the present work. Here we intend to use EEG data to classify two classes, corresponding to left and right hand movement, and EOG data to classify two classes corresponding to left and right sided eyeball movement. We will use these classifications independently first and then combine these with different weightage to find if a better and reliable control is possible. For this purpose offline classification of motor imaginary EEG data of a subject was carried out extracting features using Common Spatial Pattern (CSP) and classifying using Linear Discriminative Analysis. The independent EEG motor imaginary data classification resulted in 89.8% of accuracy in 10 fold one leave out cross validation. The EOG eyeball movement produces distinctive signals of opposite polarities and is classified using a simple discriminant type classification resulting in 100% accuracy. However, using EOG solely is not acceptable as there always will be unintentional eye movement giving false commands. Combining both EEG and EOG with different weightage to the two classifications produced varied degrees of improvement. For 50% weightage to both resulted in 100% accuracy, without any error, and this may be accepted as a practical solution because the chances of unintentional false commands will be very rare. Therefore, a combination of EOG and BCI may lead to a greater reliability in terms of avoidance of undesired control signals.Bangladesh Journal of Medical Physics Vol.10 No.1 2017 47-58

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Crossley, Kelly J., Marcus B. Nicol, Jonathan J. Hirst, David W. Walker i Geoffrey D. Thorburn†. "Suppression of arousal by progesterone in fetal sheep". Reproduction, Fertility and Development 9, nr 8 (1997): 767. http://dx.doi.org/10.1071/r97074.

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The high rate of progesterone synthesis by the placenta in late gestation exposes the ovine fetus to high concentrations of progesterone and its metabolites that may affect activity of the fetal brain. The aim of this study was to determine the effect of inhibiting maternal progesterone synthesis on sleep–wake activity in fetal sheep. Fetal and maternal vascular catheters, a fetal tracheal catheter, and electrodes for recording fetal electrocortical (ECoG), electro-ocular (EOG) and nuchal muscle electromyographic (EMG) activity were implanted. At 128–131 days gestation, progesterone production was inhibited by an injection of trilostane (50 mg), a 3β-hydroxysteroid dehydrogenase inhibitor. Vehicle solution or progesterone (3 mg h -1 ) was then infused into the ewe between 6 and 12 h after the trilostane treatment. Maternal progesterone concentrations were significantly reduced from 1–24 h after trilostane treatment (P < 0·05) when followed by vehicle infusion. Fetal breathing movements (FBM), EOG, nuchal muscle EMG, and behavioural arousal increased 12 h after trilostane treatment (P < 0 · 05). In contrast, there was no change in fetal arousal, EOG, EMG or FBM activities when progesterone was infused after the trilostane treatment. These findings show that progesterone can influence fetal behaviour, and indicates that normal progesterone production tonically suppresses arousal, or wakefulness in the fetus.

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Belkhiria, Chama, Atlal Boudir, Christophe Hurter i Vsevolod Peysakhovich. "EOG-Based Human–Computer Interface: 2000–2020 Review". Sensors 22, nr 13 (29.06.2022): 4914. http://dx.doi.org/10.3390/s22134914.

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Electro-oculography (EOG)-based brain–computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users’ intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user’s communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user’s intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries.

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Wissel, Tobias, i Ramaswamy Palaniappan. "Considerations on Strategies to Improve EOG Signal Analysis". International Journal of Artificial Life Research 2, nr 3 (lipiec 2011): 6–21. http://dx.doi.org/10.4018/jalr.2011070102.

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Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions.

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Moretti, D. "Computerized processing of EEG–EOG–EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials". International Journal of Psychophysiology 47, nr 3 (marzec 2003): 199–216. http://dx.doi.org/10.1016/s0167-8760(02)00153-8.

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Gang, Gyeong Woo, i Tae Seon Kim. "Development of Human-machine Interface based on EMG and EOG". Journal of the Institute of Electronics Engineers of Korea 50, nr 12 (25.12.2013): 129–37. http://dx.doi.org/10.5573/ieek.2013.50.12.129.

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Aziz, Fayeem, Hamzah Arof, Norrima Mokhtar i Marizan Mubin. "HMM based automated wheelchair navigation using EOG traces in EEG". Journal of Neural Engineering 11, nr 5 (4.09.2014): 056018. http://dx.doi.org/10.1088/1741-2560/11/5/056018.

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Doman, Jack, i David J. Kupfer. "Computer analysis of EEG, EOG, and NPT activity during sleep". International Journal of Bio-Medical Computing 23, nr 3-4 (grudzień 1988): 191–200. http://dx.doi.org/10.1016/0020-7101(88)90013-x.

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Paul, Gordon Mark, Fan Cao, Russel Torah, Kai Yang, Steve Beeby i John Tudor. "A Smart Textile Based Facial EMG and EOG Computer Interface". IEEE Sensors Journal 14, nr 2 (luty 2014): 393–400. http://dx.doi.org/10.1109/jsen.2013.2283424.

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Hamada, Yuya, Minoru SASAKI, Atsushi Ishida i Satoshi Ito. "5520 Servo system development by using EMG and EOG signals". Proceedings of the JSME annual meeting 2006.5 (2006): 573–74. http://dx.doi.org/10.1299/jsmemecjo.2006.5.0_573.

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Hosni, Sarah M., Howida A. Shedeed, Mai S. Mabrouk i Mohamed F. Tolba. "EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface". Neuroinformatics 17, nr 3 (27.10.2018): 323–41. http://dx.doi.org/10.1007/s12021-018-9402-0.

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Zhang, T., C. Chen i M. Nakamura. "Reliable EOG signal-based control approach with EEG signal judgment". Artificial Life and Robotics 14, nr 2 (listopad 2009): 195–98. http://dx.doi.org/10.1007/s10015-009-0652-7.

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Chang, Tae G., Jack R. Smith i Jose C. Principe. "An expert system for multichannel sleep EEG/EOG signal analysis". ISA Transactions 28, nr 1 (styczeń 1989): 45–51. http://dx.doi.org/10.1016/0019-0578(89)90056-6.

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