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Academic literature on the topic 'EOG'

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Published: 4 June 2021
Last updated: 4 May 2024

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Journal articles on the topic "EOG"

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Reilly, Richard B., and T. Clive Lee. "Electrograms (ECG, EEG, EMG, EOG)." Technology and Health Care 18, no. 6 (November 19, 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, no. 1 (January 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, and Amiya Patanaik. "271 Sleep staging performance of a signal-agnostic cloud-based real-time sleep analytics platform." Sleep 44, Supplement_2 (May 1, 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, and 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, no. 5 (May 10, 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, and Aleksandra Kawala-Sterniuk. "Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach—Part III: Other Biosignals." Sensors 21, no. 18 (September 10, 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, and N. Sriraam. "Design of EOG Signal Acquisition System Using Virtual Instrumentation." International Journal of Measurement Technologies and Instrumentation Engineering 4, no. 1 (January 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, and Wei Chen. "The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods." Bioengineering 10, no. 5 (May 10, 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, and Rakesh Kumar Mahendran. "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data." Diagnostics 13, no. 17 (September 3, 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, and Abraham Otero. "Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review." Sensors 19, no. 21 (October 29, 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, no. 11 (2017): 1006–9. http://dx.doi.org/10.2493/jjspe.83.1006.

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Dissertations / Theses on the topic "EOG"

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Yue, Chongshi. "EOG Signals in Drowsiness Research." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81761.

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Blink waveform in electrooculogram (EOG) data was used to develop and adjust the method of drowsiness detection in drivers. The origins of some other waveforms in EOG signal were not very clearly understood. The purpose of this thesis work is to study the EOG signal and give explanation of different kind of waveforms in EOG signal, and give suggestions to improve the blink detection algorithm. The road driving test video records and synchronized EOG signal were used to build an EOG library. By comparing the video record of the driver’s face and the EOG data, the origin of the unknown waveforms were discovered and related with the driver’s behavior. Literature descriptions were given to explain the EOG signal. The EOG library is the main result of this project. It organized by different types of EOG signal. Description and explanation were given for each type of waveform, as well as some examples. The knowledge gained from the previous research review and the EOG library gives some improvement suggestions for the blink detection algorithm. These suggestions still need to be verified in practical way.
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Kelly, Graham. "Development of a compact, low-cost wireless device for biopotential acquisition." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3559.

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A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both as a functional data acquisition system and as a template for further development based on its publicly-available schematics and computer-aided design (CAD) files. The design will be made available at the GitHub repository https://github.com/kellygs/eeg.
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Noureddin, Borna. "Online removal of eye movement and blink artifacts from EEG signals without EOG." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27818.

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In this thesis, two novel methods are presented for online removal of ocular artifacts (OA) from EEG without the need for EOG electrodes attached to the face. Both methods are fully automated and can remove the effects of both eye movements and blinks. The first method employs a high speed eye tracker and three frontal EEG electrodes as a reference to any nonlinear adaptive filter to remove OAs without any calibration. For the filters considered, at some frontal electrodes, using the eye tracker-based reference was shown to significantly (p < .05) improve the ability to remove OAs over using either EOG or only frontal EEG as a reference. Using an eye tracker provides the means for recording point-of-gaze and blink dynamics simultaneously with EEG, which is often desired or required in clinical studies and a variety of human computer interface applications. The second method uses a biophysical model of the head and movement of the eyes to remove OAs. It only requires a short once-per-subject calibration and does not require subject-specific MRI. It was compared to four existing methods, and was shown to perform consistently over a variety of tasks. In removing both saccades and blinks, it removed more than 4 times as much OA as the other methods. In terms of distortion, it was the only method that never removed more power than was present in the original EEG. To carry out the above studies, several related original investigations and developments were needed. These included a novel algorithm to extract the blink time course from eye tracker images, a new measure of OA removal distortion, a high speed eye tracker recording system, a study to determine whether frontal EEG could be used to replace EOG for OA removal and studies of the frequency content of blinks, the effects of an electromagnetic sensor on EEG, and the effects of varying mental states on OA removal methods. In summary this thesis has helped pave the way towards a real-time EEG-based human interface that is free of OAs and does not require EOG electrodes in its operation.
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Chocos, Ruiz Miguel Edgardo. "Estudo de filtros adaptativos para a remoção de artefatos de EOG em sinais de EEG." Florianópolis, SC, 1999. http://repositorio.ufsc.br/xmlui/handle/123456789/81217.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico.
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Hapuarachchi, Pasan. "Feature selection and artifact removal in sleep stage classification." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2879.

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The use of Electroencephalograms (EEG) are essential to the analysis of sleep disorders in patients. With the use of electroencephalograms, electro-oculograms (EOG), and electromyograms (EMG), doctors and EEG technician can make conclusions about the sleep patterns of patients. In particular, the classification of the sleep data into various stages, such as NREM I-IV, REM, Awake, is extremely important. The EEG signal itself is highly sensitive to physiological and non-physiological artifacts. Trained human experts can accommodate for these artifacts while they are analyzing the EEG signal.

However, if some of these artifacts are removed prior to analysis, their job will be become easier. Furthermore, one of the biggest motivations, of our team's research is the construction of a portable device that can analyze the sleep data as they are being collected. For this task, the sleep data must be analyzed completely automatically in order to make the classifications.

The research presented in this thesis concerns itself with the denoising and the feature selection aspects of the teams' goals. Since humans are able to process artifacts and ignore them prior to classification, an automated system should have the same capabilities or close to them. As such, the denoising step is performed to condition the data prior to any other stages of the sleep stage neoclassicisms. As mentioned before, the denoising step, by itself, is useful to human EEG technicians as well.

The denoising step in this research mainly looks at EOG artifacts and artifacts isolated to a single EEG channel, such as electrode pop artifacts. The first two algorithms uses Wavelets exclusively (BWDA and WDA), while the third algorithm is a mixture of Wavelets and In- dependent Component Analysis (IDA). With the BWDA algorithm, determining consistent thresholds proved to be a difficult task. With the WDA algorithm, the performance was better, since the selection of the thresholds was more straight-forward and since there was more control over defining the duration of the artifacts. The IDA algorithm performed inferior to the WDA algorithm. This could have been due to the small number of measurement channels or the automated sub-classifier used to select the denoised EEG signal from the set of ICA demixed signals.

The feature selection stage is extremely important as it selects the most pertinent features to make a particular classification. Without such a step, the classifier will have to process useless data, which might result in a poorer classification. Furthermore, unnecessary features will take up valuable computer cycles as well. In a portable device, due to battery consumption, wasting computer cycles is not an option. The research presented in this thesis shows the importance of a systematic feature selection step in EEG classification. The feature selection step produced excellent results with a maximum use of just 5 features. During automated classification, this is extremely important as the automated classifier will only have to calculate 5 features for each given epoch.
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Karlsson, Johanna. "Identifying patterns in physiological parameters of expert and novice marksmen in simulation environment related to performance outcomes." Thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139589.

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The goal of this thesis is to investigate if it is possible to use measurements of physiological parameters to accelerate learning of target shooting for novice marksmen in Saab’s Ground combat indoor trainer (GC-IDT). This was done through a literature study that identified brain activity, eye movements, heart activity, muscle activity and breathing as related to shooting technique. The sensors types Electroencephalography (EEG), Electroocculography (EOG), Electrocardiogram (ECG), Electromyography (EMG) and impedance pneumography (IP) were found to be suitable for measuring the respective parameters in the GC-IDT. The literature study also showed that previous studies had found differences in the physiological parameters in the seconds leading up to the shot when comparing experts and novices. The studies further showed that it was possible to accelerate learning by giving feedback to the novices about their physiological parameters allowing them to mimic the behavior of the experts. An experiment was performed in the GC-IDT by measuring EOG, ECG, EMG and IP on expert and novice marksmen to investigate if similar results as seen in previous studies were to be found. The experiment showed correlation between eye movements and shooting score, which was in line with what previous studies had shown. The respiration measurement did not show any correlation to the shooting scores in this experiment, it was however possible to see a slight difference between expert and novices. The other measurements did not show any correlation to the shooting score in this experiment. In the future, further experiments needs to be made as not all parameters could be explored in depth in this experiment. Possible improvements to such experiments are i.e. increasing the number of participants and/or the number of shots as well as marking shots automatically in the data and increasing the time between shots.
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Peiris, Malik Tivanka Rajiv. "Lapses in Responsiveness: Characteristics and Detection from the EEG." Thesis, University of Canterbury. Electrical and Computer Engineering, 2008. http://hdl.handle.net/10092/1261.

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Performance lapses in occupations where public safety is paramount can have disastrous consequences, resulting in accidents with multiple fatalities. Drowsy individuals performing an active task, like driving, often cycle rapidly between periods of wake and sleep, as exhibited by cyclical variation in both EEG power spectra and task performance measures. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system. Additionally, the project also sought to achieve an increased understanding of the characteristics of lapses in responsiveness in normal subjects. A study was conducted to determine EEG and/or EOG cues (if any) that expert raters use to detect lapses that occur during a psychomotor vigilance task (PVT), with the subsequent goal of using these cues to design an automated system. A previously-collected dataset comprising physiological and performance data of 10 air traffic controllers (ATCs) was used. Analysis showed that the experts were unable to detect the vast majority of lapses based on EEG and EOG cues. This suggested that, unlike automated sleep staging, an automated lapse detection system needed to identify features not generally visible in the EEG. Limitations in the ATC dataset led to a study where more comprehensive physiological and performance data were collected from normal subjects. Fifteen non-sleep-deprived male volunteers aged 18-36 years were recruited. All performed a 1-D continuous pursuit visuomotor tracking task for 1 hour during each of two sessions that occurred between 1 and 7 weeks apart. A video camera was used to record head and facial expressions of the subject. EEG was recorded from electrodes at 16 scalp locations according to the 10-20 system at 256 Hz. Vertical and horizontal EOG was also recorded. All experimental sessions were held between 12:30 and 17:00 hours. Subjects were asked to refrain from consuming stimulants or depressants, for 4 h prior to each session. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. The study also showed that lapsing and tracking error increased during the first 30 or so min of a 1-h session, then decreased during the remaining time, despite the absence of external temporal cues. EEG spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. Thus, complete lapses in responsiveness are a frequent phenomenon in normal subjects - even when not sleep-deprived - undertaking an extended, monotonous, continuous visuomotor task. This is the first study to investigate and report on the characteristics of complete lapses of responsiveness during a continuous tracking task in non-sleep-deprived subjects. The extent to which non-sleep-deprived subjects experience complete lapses in responsiveness during normal working hours was unexpected. Such findings will be of major concern to individuals and companies in various transport sectors. Models based on EEG power spectral features, such as power in the traditional bands and ratios between bands, were developed to detect the change of brain state during behavioural microsleeps. Several other techniques including spectral coherence and asymmetry, fractal dimension, approximate entropy, and Lempel-Ziv (LZ) complexity were also used to form detection models. Following the removal of eye blink artifacts from the EEG, the signal was transformed into z-scores relative to the baseline of the signal. An epoch length of 2 s and an overlap of 1 s (50%) between successive epochs were used for all signal processing algorithms. Principal component analysis was used to reduce redundancy in the features extracted from the 16 EEG derivations. Linear discriminant analysis was used to form individual classification models capable of detecting lapses using data from each subject. The overall detection model was formed by combining the outputs of the individual models using stacked generalization with constrained least-squares fitting used to determine the optimal meta-learner weights of the stacked system. The performance of the lapse detector was measured both in terms of its ability to detect lapse state (in 1-s epochs) and lapse events. Best performance in lapse state detection was achieved using the detector based on spectral power (SP) features (mean correlation of φ = 0.39 ± 0.06). Lapse event detection performance using SP features was moderate at best (sensitivity = 73.5%, selectivity = 25.5%). LZ complexity feature-based detector showed the highest performance (φ = 0.28 ± 0.06) out of the 3 non-linear feature-based detectors. The SP+LZ feature-based model had no improvement in performance over the detector based on SP alone, suggesting that LZ features contributed no additional information. Alpha power contributed the most to the overall SP-based detection model. Analysis showed that the lapse detection model was detecting phasic, rather than tonic, changes in the level of drowsiness. The performance of these EEG-based lapse detection systems is modest. Further research is needed to develop more sensitive methods to extract cues from the EEG leading to devices capable of detecting and/or predicting lapses.
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Mathew, Blesy Anu. "ENTROPY OF ELECTROENCEPHALOGRAM (EEG) SIGNALS CHANGES WITH SLEEP STATE." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/203.

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We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Entropy, wereevaluated for their ability to discriminate sleep stages. SpEn changes with sleep state, beinglargest in Wake. Stage 3/4 had the smallest SpEn (0.57??0.11) normalized to Wake values,followed by Stage 2 (0.72??0.09), REM (0.75??0.1) and Stage 1 (0.89??0.05). This pattern wasconsistent in all the polysomnogram records analyzed. Similar pattern was observed in leadO1A2 as well. We conclude that SpEn may be useful as part of a montage for assessing sleepstate. We analyzed data from sleep apnea subjects having obstructive and central apnea eventsand have made some preliminary observations; the SpEn values were more similar across sleepstages and also high correlation with oxygen saturation was observed.
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Hartmann, Daniel Michael. "Einsatz eines superfundierten Retina-RPE-Choroidea Präparats vom Haushuhn (Gallus domesticus) zur Untersuchung pharmakologischer Wirkungen mittels in vitro elektroretinographischer Erfassung (ERG und EOG) von okulären Funktionen." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11679860.

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Escola, Henri. "Analyse et traitement de signaux physiologiques pour la mesure de l'action de substances pharmacologiques." Compiègne, 1993. http://www.theses.fr/1993COMPD593.

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L'objectif de ce travail est de proposer des méthodes d'analyse de de traitement des signaux physiologiques (EEG, EMG, EOG) tant diurnes (Chap. II) que nocturnes (Chap. III, IV, V). Nous présentons dans un premier temps une méthode d'analyse qualitative basée sur la notion de distance dans un espace multidimensionnel qui peut venir compléter la cartographie cérébrale classique (Chap. II). Pour l'étude du sommeil, nous proposons de décrire l'activité EEG par plusieurs types de paramètres continus. Nous montrons également comment des variables capables de mettre en évidence les effets de substances pharmacologiques peuvent être extraites de ces différents paramètres (Chap. III). Pour caractériser le signal EOG, Nous avons développé une méthode de détection automatique des mouvements oculaires permettant de mesurer l'action de deux substances sur cette activité (Chap. IV). Pour finir, nous tentons de faire une synthèse des résultats obtenus dans les chapitres précédents en jetant les bases d'une analyse ou les différents signaux sont pris simultanément en considération. Plusieurs techniques (corrélation, indice de dispersion, prédiction non-linéaire) sont évaluées toujours dans le cadre d'une étude pharmacologique (Chap. V)
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Books on the topic "EOG"

1

Hereford, Jane. The competitive edge: Passing the North Carolina Eighth Grade EOG Test in Reading. Raleigh, NC: Contemporary Pub. Co. of Raleigh, 1997.

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Hereford, Jane. The competitive edge: Passing the North Carolina eighth grade EOG test in mathematics. Raleigh, NC: Contemporary Pub. Co. of Raleigh, 1997.

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Slack, J. M. W. Egg & Ego. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1420-5.

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Genquan, Feng. EKG and EEG multiphase information analysis. [New York]: American Medical Publishers, 1992.

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Egg & ego: An almost true story of life in the biology lab. New York: Springer, 1999.

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Budiman, Fajar. Eng ing eng. [Jakarta]: Jayakarta, 1990.

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Graeve, Laurent de. Ego, ego: Roman. Monaco: Editions du Rocher, 1999.

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Landt, Artur. Canon EOS Rebel T2: Includes, EOS Rebel K2, EOS Rebel TI, EOS 300X, EOS 3000V, EOS 300V. New York: Lark Books, 2005.

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Zoehfeld, Kathleen Weidner. Disney's big egg, little egg. New York: Mouse Works, 1998.

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Weiss, Nicki. An egg is an egg. New York, N.Y: Trumpet Club, 1992.

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Book chapters on the topic "EOG"

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Geller, Andrew M., Catherine M. Osborne, and Robert L. Peiffer. "The ERG, EOG, and VEP in Rats." In Ocular Toxicology, 7–25. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1887-7_2.

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Mishra, Ayushi, Vikrant Bhateja, Aparna Gupta, Apoorva Mishra, and Suresh Chandra Satapathy. "Feature Fusion and Classification of EEG/EOG Signals." In Advances in Intelligent Systems and Computing, 793–99. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3600-3_76.

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Champaty, Biswajeet, D. N. Tibarewala, Biswajit Mohapatra, and Kunal Pal. "Development of EOG and EMG-Based Multimodal Assistive Systems." In Medical Imaging in Clinical Applications, 285–310. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33793-7_13.

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Gupta, Aparna, Vikrant Bhateja, Apoorva Mishra, and Ayushi Mishra. "Autoregressive Modeling-Based Feature Extraction of EEG/EOG Signals." In Information and Communication Technology for Intelligent Systems, 731–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1747-7_72.

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Garcés Correa, Agustina, Lorena L. Orosco, and Eric Laciar Leber. "Analysis of Parameters that Characterize Drowsiness Based on EEG, ECG and EOG Records." In IFMBE Proceedings, 461–67. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51723-5_57.

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Kosmyna, Nataliya, Arnav Balyan, and Eugene Hauptmann. "Decoding Visual Imagery Using EEG/EOG Glasses: A Pilot Study." In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2, 415–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18458-1_29.

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Kosmyna, Nataliya, Arnav Balyan, and Eugene Hauptmann. "Target Speaker Detection with EEG/EOG Glasses: A Pilot Study." In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2, 433–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18458-1_30.

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Abbas, Sherif N., and M. Abo-Zahhad. "Eye Blinking EOG Signals as Biometrics." In Signal Processing for Security Technologies, 121–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47301-7_5.

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Edlinger, Guenter, Christoph Kapeller, Arnau Espinosa, Sergi Torrellas, Felip Miralles, and Christoph Guger. "Multi-modal Computer Interaction for Communication and Control Using EEG, EMG, EOG and Motion Sensors." In Universal Access in Human-Computer Interaction. Design Methods, Tools, and Interaction Techniques for eInclusion, 633–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39188-0_68.

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Bhateja, Vikrant, Aparna Gupta, Apoorva Mishra, and Ayushi Mishra. "Artificial Neural Networks Based Fusion and Classification of EEG/EOG Signals." In Advances in Intelligent Systems and Computing, 141–48. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3338-5_14.

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Conference papers on the topic "EOG"

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Hirayama, Yoshikazu, Tomomi Takashina, Yuichi Watanabe, Kensaku Fukumoto, Miyuki Yanagi, Ryota Horie, and Michiko Ohkura. "Physiological Signal- Driven Camera Using EOG, EEG, and ECG." In 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). IEEE, 2019. http://dx.doi.org/10.1109/aciiw.2019.8925063.

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Chernyshov, George, Kirill Ragozin, Benjamin Tag, and Kai Kunze. "EOG Glasses." In MobileHCI '19: 21st International Conference on Human-Computer Interaction with Mobile Devices and Services. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338286.3344418.

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Kim, Yun Seong, Haet Bit Lee, Jung Soo Kim, Hyun Jae Baek, Myung Suk Ryu, and Kwang Suk Park. "ECG, EOG detection from helmet based system." In 6th International Special Topic Conference on Information Technology Applications in Biomedicine, 2007. IEEE, 2007. http://dx.doi.org/10.1109/itab.2007.4407378.

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Anh-Dao, Nguyen Thi, Tran Duc-Nghia, Nguyen Thi-Hao, Tran Duc-Tan, and Nguyen Linh-Trung. "An effective procedure for reducing EOG and EMG artefacts from EEG signals." In 2013 International Conference on Advanced Technologies for Communications (ATC 2013). IEEE, 2013. http://dx.doi.org/10.1109/atc.2013.6698131.

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Ahamed, Md Asif, Md Asraf-Ul Ahad, Md Hanif Ali Sohag, and Mohiuddin Ahmad. "Development of low cost wireless biosignal acquisition system for ECG EMG and EOG." In 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT). IEEE, 2015. http://dx.doi.org/10.1109/eict.2015.7391945.

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Bulling, Andreas, Daniel Roggen, and Gerhard Tröster. "Wearable EOG goggles." In the 27th international conference extended abstracts. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1520340.1520468.

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P L, Lekshmylal, Shiny G, and Ashalatha Radhakrishnan. "Removal of EOG and EMG artifacts from EEG signals using blind source separation methods." In 2023 International Conference on Control, Communication and Computing (ICCC). IEEE, 2023. http://dx.doi.org/10.1109/iccc57789.2023.10165575.

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Olesen, Simon Dahl Thorsager, Rig Das, Mathias Dizon Olsson, Muhammad Ahmed Khan, and Sadasivan Puthusserypady. "Hybrid EEG-EOG-based BCI system for Vehicle Control." In 2021 9th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2021. http://dx.doi.org/10.1109/bci51272.2021.9385300.

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Vehkaoja, A. T., J. A. Verho, M. M. Puurtinen, N. M. Nojd, J. O. Lekkala, and J. A. Hyttinen. "Wireless Head Cap for EOG and Facial EMG Measurements." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615824.

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Kalaganis, Fotis P., Manuel Seet, Kostas Georgiadis, Vangelis P. Oikonomou, Nikos A. Laskaris, Spiros Nikolopoulos, Ioannis Kompatsiaris, Maria Panou, Andrei Dragomir, and Anastasios Bezerianos. "Reconstructing EOG From EEG Timeseries: A Spatial Filtering Approach." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630320.

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Reports on the topic "EOG"

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La Haye, Robert. National Spherical Torus Experiment Upgrade Collaborative Research on Configuration Optimization of Advanced Operating Scenarios and Control Including Macroscopy Stability EOG for Period 3/1/14 through 2/28/18. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1439061.

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Hyde, Peter Alden. EOC Photos. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1565815.

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Li, Shangchen, Hongxun Ruan, Sheridan Titman, and Haotian Xiang. ESG Spillovers. Cambridge, MA: National Bureau of Economic Research, May 2023. http://dx.doi.org/10.3386/w31248.

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Jensen, Melanie, Steven Schlasner, Kerryanne Leroux, Charles Gorecki, and Nicholas Azzolina. Comparison of Non-EOR and EOR Life Cycle Assessments. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1874451.

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Bright, Ashleigh Bright. Egg producer and egg buyer disconnect: Exploring barriers and levers to increase cage-free egg production in China. Tiny Beam Fund, December 2022. http://dx.doi.org/10.15868/socialsector.41288.

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Scofield, Thomas C., Elizabeth Walter, and Samuel J. Livingstone. Epidemic Outbreak Surveillance (EOS). Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada483621.

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Zhao, Jeffrey. ENG 572 Final Report. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1645179.

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Morton, Paul E., and Glenn F. Wilson. Backpropagation and EEG Data. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada279073.

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Glickman, Matthew R., and Akaysha Tang. EEG analyses with SOBI. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/978914.

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Zhao, Jeffrey. ENG 572 Interim Report. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1863849.

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