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Electrooculograms

Electrooculograms (EOGs) are a non-invasive technique used to measure eye movements and position.
EOGs record the electrical potentials generated by the movement of the eyes, providing insights into visual processing and oculomotor control.
This technique is widely used in various research and clinical applications, such as studying eye-related disorders, sleep analysis, and human-computer interaction.
PubCompare.ai's innovative AI-driven platform can optimize your EOG research by helping you locate relevant protocols from literature, preprints, and patents, and leveraging AI-powered comparisons to identify the most accurate and reproducible methods.
Unlock new heights in your EOG research with PubCompare.ai's suite of tools and insights.

Most cited protocols related to «Electrooculograms»

Screening for Obstructive Sleep Apnea in Preoperative Patients

The study was conducted in the preoperative clinics of Toronto Western Hospital and Mount Sinai Hospital, Toronto, Ontario, Canada. Institutional Review Board approvals were obtained from both institutions (MSH: 06-0143-E and 07-0183-E; UHN: 06-0135-AE and 07-0515-AE). Patients aged 18 yr or older, who were ASA I–IV, and were undergoing elective procedures in general surgery, gynaecology, orthopaedics, urology, plastic surgery, ophthalmology, or spinal surgery were included in the screening process and approached for consent by the research assistants for the preoperative polysomnograpy (PSG). Patients who were unwilling or unable to give informed consent or patients who were expected to have abnormal EEG findings (e.g. brain tumour, epilepsy surgery, patients with deep brain stimulator) were excluded.
All the patients were asked to complete the STOP questionnaire.^{11 (link)} Information concerning BMI, age, neck circumference, and gender (Bang) were collected by a research assistant. In the initial 2 yr period of the study, the patients were invited to undergo a laboratory PSG. During the subsequent 2 yr of the study, the patients underwent a portable PSG study at home. The results of the PSG were used to evaluate the various scores of the STOP-Bang questionnaire.
The portable PSG was performed with a level 2 portable sleep device (Embletta X100) which is shown to be a reliable alternative for standard PSG in surgical patients.¹⁴ The PSG recordings were performed at the patients’ home. The recording montage consisted of two EEG channels (C3 and C4), electrooculogram (left or right), and chin muscle EMGs. Thoracic and abdominal respiratory effort bands, body position sensors, and pulse oximeter were also used.
The device was attached to patients by a well-trained PSG technician at their home and the overnight recordings were unattended. The patients were advised on how to remove the device which was picked up the next morning from the patients’ home by the same sleep technician. A certified PSG technologist who was blinded to the study information analysed the PSG. The manual scoring was performed using Somnologia Studio 5.0 as the scoring platform. Manual scoring was performed according to the Manual of the American Academy of Sleep Medicine.¹⁵The laboratory PSG was performed overnight and patients went to bed at their usual bedtime. A standard EEG montage consisting of EEG, electrooculogram, submental EMG, and ECG obtained with surface electrodes were used to collect the sleep architectural data. A pulse oximeter measured the oxygen saturation. Additional recordings included the respiratory effort by thoraco-abdominal excursion, respiratory inductive plethysmography, and oronasal airflow.
A certified polysomnographic technologist scored the polysomnographic recordings under the supervision of a sleep physician who assessed and approved the reports. The technologist was blinded to the results of the STOP-Bang questionnaire and other clinical information about the patients. The sleep stages and apnoea–hypopnea index (AHI) were scored according to the American Academy of Sleep Medicine Task Force recommendations.^{16 (link)}The diagnosis of OSA was based on an AHI >5 with fragmented sleep and daytime sleepiness. The severity of OSA with both laboratory and portable PSG was classified based on the AHI values: >5–15 as mild OSA, >15–30 as moderate OSA, and >30 as severe OSA.^{15 16 (link)}

Chung F., Subramanyam R., Liao P., Sasaki E., Shapiro C, & Sun Y. (2012). High STOP-Bang score indicates a high probability of obstructive sleep apnoea. BJA: British Journal of Anaesthesia, 108(5), 768-775.

Publication 2012

Abdomen Apnea Brain Brain Neoplasms Chin Conditioning, Psychology Diagnosis Electrooculograms Epilepsy Ethics Committees, Research Gender Medical Devices Muscle Tissue Neck Operative Surgical Procedures Orthopedic Surgical Procedures Oxygen Saturation Patients Pharmaceutical Preparations Physicians Plastic Surgical Procedures Plethysmography Pulse Rate Respiratory Rate Sleep Sleep Stages Supervision

EEG Data Preprocessing and Artifact Removal

This paper presents a re-analysis of data we reported previously (Hipp et al., 2011 (link)). We recorded the continuous EEG from 126 scalp sites and the electrooculogram (EOG) from two sites below the eyes all referenced against the nose tip (sampling rate: 1000 Hz; high-pass: 0.01 Hz; low-pass: 250 Hz; Amplifier: BrainAmp, BrainProducts, Munich, Germany; Electrode cap: Electrodes: sintered Ag/AgCl ring electrodes mounted on an elastic cap, Falk Minow Services, Herrsching, Germany). Electrode impedances were kept below 20 kΩ. Offline, the data were high-pass filtered (4 Hz, Butterworth filter of order 4) and cut into trials of 2.5 s duration centered on the presentation of the sound (−1.25 to 1.25 s). First, trials with eye movements, eye blinks, or strong muscle activity were identified by visual inspection and rejected from further analysis (trials retained for further analyses n = 345 ± 50, mean ± s.d.). Next, we used independent component analysis (FastICA, http://www.cis.hut.fi/projects/ica/fastica/; Hyvärinen, 1999 (link)) to remove artifactual signal components (Jung et al., 2000 (link); Keren et al., 2010 (link)). The removed artifactual components constituted facial muscle components (n = 45.8 ± 7.84, mean ± s.d.), microsaccadic artifact components (n = 1.2 ± 0.82, mean ± s.d.), auricular artifact components (O'Beirne and Patuzzi, 1999 (link)) (n = 0.5 ± 0.83, mean ± s.d.), and heart beat components (n = 0.5 ± 0.59, mean ± s.d.). Alternatively to ICA, we accounted for microsaccadic artifacts by removing confounded data sections identified in the radial EOG using the approach and template described in Keren et al. (2010 (link)) (Threshold: 3.5). Importantly, for this analysis step, we did not reject entire trials containing a microsaccadic artifact (79 ± 18%, mean ± s.d., of trials contained at least one saccadic spike artifact), but only invalidated the data in the direct vicinity of detected artifacts (±0.15 s). Whenever the window for time-frequency transform overlapped with invalidated data (see spectral analysis below), it was rejected from further analysis. As a consequence, spectral estimates were based on varying amount of data across time and frequency. We derived the radial EOG as the difference between the average of the two EOG channels and a parietal EEG electrode at the Pz position of the 10–20-system. Notably, rejection based on the radial EOG may miss saccadic spike artifacts of small amplitude that can be detected with high-speed eyetracking (Keren et al., 2010 (link)). However, the fact that we did not find any significant saccadic spike artifacts after radial EOG based rejection at those source locations that before cleaning best captured these artifacts (cf. Figure 7C) suggests that potentially remaining artifacts are small.

Hipp J.F, & Siegel M. (2013). Dissociating neuronal gamma-band activity from cranial and ocular muscle activity in EEG. Frontiers in Human Neuroscience, 7, 338.

Publication 2013

Blinking Electrooculograms Eye Movements Facial Muscles Impedance, Electric Muscle Strength Nose Pulse Rate Scalp Sound

EEG Acquisition Protocol for Cognitive Studies

EEG was recorded from 57 channels relative to average reference using a QuickAmp 72 (Brain Products). Channels (FPz, FP1, FP2, AFz, AF7, AF3, AF4, AF8, Fz, F7, F3, F4, F8, FCz, FT9, FC5, FC3, FC1, FC2, FC4, FC6, FT1, T7, C5, C3, C1, C2, C4, C6, T8, CPz, CP5, CP3, CP1, CP2, CP4, CP6, Pz, P7, P3, P1, P2, P4, P8, Oz, PO9, PO7, PO3, PO4, PO8, PO1, Oz, O1, O2, I1, I2, Cz) were positioned following the 10-20-system (Jasper, 1958). Additional electrodes were positioned at the mastoids (M1, M2) and four electrodes were used to record the electrooculogram (EOG) from positions below (IO2) and above the right eye (SO2) and from the outer canthi (LO1, LO2). All channels (EEG,EOG) were recorded with respect to the same reference (average reference). Impedances were kept below 10 kOhm. Sampling rate was 500 Hz (no highpass, lowpass 135 Hz).

Hoffmann S, & Falkenstein M. (2008). The Correction of Eye Blink Artefacts in the EEG: A Comparison of Two Prominent Methods. PLoS ONE, 3(8), e3004.

Publication 2008

Brain Electrooculograms Impedance, Electric Process, Mastoid

EEG Analysis of P300 Response

A realistic forward model was created for each subject using a three-layer BEM applied to individual MRI data. Conductivities of brain, skull and skin were 1, 0.0125 and 1 S/m. EEG electrodes were aligned on the head model using the locations of the three fiducials and the head shape extracted from the MR image, using the dedicated algorithm implemented in SPM8.
The experimental EEG data were filtered in the band 0.5-20Hz and further processed using independent component analysis (ICA) for the removal of ocular and muscular artifacts (Mantini et al 2008 (link)). After ICA decomposition, the artifactual ICs were automatically detected by correlating their power time-courses with the power time courses of the electric reference signals: the horizontal electrooculogram (hEOG), the vertical electrooculogram (vEOG) and the electromyogram (MEG) at the base of the neck. Then, we applied each of the different re-referencing techniques on the cleaned EEG data. Finally, we calculated ERPs for the rare events only, as these are the ones supposedly showing the P300 response (Picton 1992 (link), Mantini et al 2009 (link)). Based on the results of the ERP analysis, we excluded one subject for which the P300 response was not clearly visible.
Using the preprocessed EEG signals, we measured the P300 activity and the noise levels on the averaged data. The P300 activity was defined as the maximum amplitude in the post-stimulus (from 200 to 500 ms) interval, while the noise amplitude was calculated as the average root mean square (RMS) of the signal corresponding to the pre-stimulus (from −200 to 0 ms) interval. We conducted this analysis for each single channel, but we then focused on 41 electrodes over the parietal region (see Figure S2), where the P300 (P3b) activity is typically most prominent (Picton 1992 (link)).

Liu Q., Balsters J.H., Baechinger M., van der Groen O., Wenderoth N, & Mantini D. (2015). Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model. Journal of Neural Engineering, 12(5), 056012.

Publication 2015

Brain Cranium Electric Conductivity Electricity Electromyography Electrooculograms EP300 protein, human Evoked Potentials Eye Head Muscle Tissue Neck Parietal Lobe Plant Roots Skin Strains

High-Density EEG Recording Methodology

The EEG was recorded (sampling rate 500 Hz; band-pass filter of 0.01–200 Hz) from 35 AgCl scalp electrodes (extended International 10/20 system; Easycap, BrainProducts) with reference electrodes placed at the mastoids. Signals were collected using the left mastoid as reference and re-referenced off-line to the average activity of all electrodes. Horizontal and vertical electrooculograms (EOG) were recorded with bipolar electrodes placed at the external canthi and above and below the left eye. Electrode impedance was kept below 5 kOhm for all electrodes.

Peters J.C., van den Boomen C, & Kemner C. (2017). Spatial Frequency Training Modulates Neural Face Processing: Learning Transfers from Low- to High-Level Visual Features. Frontiers in Human Neuroscience, 11, 1.

Publication 2017

Electrooculograms Process, Mastoid Scalp

Most recents protocols related to «Electrooculograms»

Multimodal Brain Activity Monitoring

Electroencephalography (EEG) in each condition was recorded using a measurement instrument with 57 electrodes (EEG-1200, Nihon Kohden, Tokyo, Japan; EasyCap GmbH, Herrsching, Germany). The layout of the electrodes was based on a modified version of the international 10–20 system. The impedance of each electrode was maintained at less than 10 kΩ. EEG signals were digitized at 1 kHz and recorded with a 0.5–300 Hz band-pass filter online. For data acquisition, EEG signals were referenced to the right earlobe and eye movements were monitored using horizontal and vertical bipolar electrooculograms (EOGs).

Kurita Y., Urakawa T, & Araki O. (2023). Theta phase coherence in visual mismatch responses involved in access processing to visual awareness. Frontiers in Human Neuroscience, 17, 1051844.

Publication 2023

Electroencephalography Electrooculograms Electrooculography Eye Movements

Multimodal Neuroimaging of Visual Attention

The MEG data were continuously recorded with an Elekta Neuromag TRIUX triple sensor system including 102 magnetometers (Elekta Neuromag Oy, Helsinki, Finland). Participants were seated below the MEG dewar in a magnetically shielded chamber. Stimulus presentation was coordinated with MATLAB (MathWorks Inc., Natick, MA, USA), and the stimuli were back-projected by a DLP-LED PROPixx projector (VPixx Technologies Inc., Saint-Bruno, QC, Canada) onto a semitransparent screen (VPixx) placed in 1-m viewing distance to the participants. Participants gave responses with the right hand via a LUMItouch response box (PhotonControl Inc., Burnaby, DC, Canada) for experiment 1 and RESPONSEPixx response boxes (VPixx) for experiments 2 and 3. The head position within the MEG dewar was determined by digitizing anatomical landmarks (nasion and bilateral preauricular points) and five spatially distributed coils (near vertex, inion, nasion, and preauricular points) with a Polhemus system (Polhemus 3Space Fastrak system). Data were sampled at 1000 Hz and band-pass–filtered online from 0.03 to 330 Hz. Signal space separation provided with the Elekta Neuromag Data Analysis Software MaxFilter was used offline for the suppression of environmental noise and spatial interferences. In addition, slight changes of the head position were tracked (coil measurements after each experimental block) and used to correct for head movements during the measurement by spatially realigning the data of each subject using MaxFilter.
EEG data were simultaneously recorded using a Neuroscan system (NeuroScan, El Paso, TX, RRID:SCR_015818) and a 32-electrode cap with mounted sintered Ag/AgCl electrodes (Easycap, Herrsching, Germany), the right mastoid was used as online reference. To record the EOG (electrooculogram), one electrode was placed below the right eye (vertical EOG), and two electrodes were placed at the outer canthi of both eyes (horizontal EOG). Contact to head surface was established using an abrasive gel (Abralyt light, Easycap), all impedances were kept below 5 kilohm. The EOG was used to track eye movements. To verify the quality of fixation, a detailed analysis of the horizontal electrooculogram (HEOG) response in experiment 1 together with a control experiment deriving a reference HEOG response when actively foveating the placeholder positions is provided in the Supplementary Materials (fig. S3). The EEG data are not reported here.

Bartsch M.V., Merkel C., Strumpf H., Schoenfeld M.A., Tsotsos J.K, & Hopf J.M. (2023). A cortical zoom-in operation underlies covert shifts of visual spatial attention. Science Advances, 9(10), eade7996.

Publication 2023

Anatomic Landmarks Electrooculograms Eye Eye Movements Head Head Movements Impedance, Electric Light Process, Mastoid Strains

Motor Imagery EEG Classification for Upper Limb Tasks

The EEG data used in this work is the “Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions” from the Giga DB dataset completed by Jeong et al. (2020a) (link). The dataset included intuitive upper limb movement data from 25 subjects, who were required to perform three types of motor tasks in a total of 11 categories, including 6 directions of arm extension movement (up, down, left, right, front, back), 3 kinds of object grasping action (cup, card, ball) and 2 kinds of wrist-twisting action (left rotation, right rotation), each type of movement was randomly executed 50 times, corresponding to 11 movements designed to be associated with each segmental movement of the arm, hand, and wrist, rather than continuous limb movements. The dataset included not only EEG data but also magnetoencephalography (EMG) and electrooculogram (EOG) data, which are collected simultaneously in the same experimental setting while ensuring no interference between them. The data were acquired using a 60-channel EEG, 7-channel EMG, and 4-channel EOG. In the current work, only motor imagery EEG data were used, the EEG sensors were placed according to the international 10–20 system, and the sampling rate was set as 2,500 Hz. Our goal is to classify the motor imagery EEG of the three types of actions, so we selected forward extension of the arm, grasping the cup, and rotation of the wrist to the left from the three types of actions for the following study.

Zhang R., Chen Y., Xu Z., Zhang L., Hu Y, & Chen M. (2023). Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network. Frontiers in Neuroscience, 17, 1129049.

Publication 2023

Electrooculograms Imagery, Guided Magnetoencephalography Movement Multimodal Imaging Upper Extremity Wrist

Polysomnography for Obstructive Sleep Apnea

In-laboratory PSG was performed utilizing ResMed Embla N7000 (ResMed, San Diego, CA, USA) and Embla MPR (Natus Medical, Pleasanton, CA, USA). PSG includes various sensors (i.e. electroencephalogram, electrooculogram, electromyogram of the chin and leg, a nasal cannula, oral–nasal thermistor, bands for the chest and abdomen, pulse oximetry, and a piezoelectric vibration sensor). All of the signals were recorded using RemLogic software (version 3.41, Embla, Thornton, CO, USA) and scored by certified PSG technologists per the Americana Academy of Sleep Medicine (AASM) Scoring Manual.²⁵ For snoring which serves as an indicator of upper airway obstruction, these events were assessed by a piezoelectric vibration sensor placed on the triangle of the neck. Technically, this sensor measures frequencies of oscillations at the skin surface, thereby generating a piezoelectric signal to represent the snoring waveform. Snoring events were defined as protruding from the background and being synchronized with breathing, except for the body movement time. Piezoelectric signals were recorded at a sampling rate of 200 Hz and with AASM-recommended filter settings (low frequency of 10 Hz and high frequency of 100 Hz). Regarding OSA severity, the AHI, defined as the number of apneic and hypopneic events of the total sleep time, was obtained, and this index was further divided into four OSA levels, namely normal (AHI: <5 times/h), mild (5 ≤ AHI <15 times/h), moderate (15≤ AHI <30 times/h), and severe (AHI ≥30 times/h).^{26 (link)}

Tsai C.Y., Liu W.T., Hsu W.H., Majumdar A., Stettler M., Lee K.Y., Cheng W.H., Wu D., Lee H.C., Kuan Y.C., Wu C.J., Lin Y.C, & Ho S.C. (2023). Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digital Health, 9, 20552076231152751.

Publication 2023

Abdomen Airway Obstruction Apnea Chest Chin Electroencephalography Electromyography Electrooculograms Movement Nasal Cannula Neck Nose Oximetry, Pulse Pharmaceutical Preparations Skin Sleep Vibration

Polysomnography Assessment of Sleep Apnea

All individuals underwent a full night (22:00 to 06:00) polysomnography testing (P Series Sleep System, Compumedics, Melbourne, Australia) to assess the following parameters: electroencephalogram, electrooculogram, submental and anterior tibialis electromyogram, electrocardiogram, arterial oxyhemoglobin saturation with pulse oximetry, nasal airflow, thoracoabdominal movement, snoring, and body position. We used the American Academy of Sleep Medicine (AASM) manual to score sleep and associated events,10 (link) including the following indices: apnea-hypopnea index (AHI; representing the number of episodes of apnea and hypopnea per hour of sleep); oxygen desaturation index (ODI; 3% oxygen desaturation index per hour of sleep); LaSO2 (lowest arterial oxyhemoglobin saturation during sleep); mean SpO2 (mean arterial oxyhemoglobin saturation during sleep); and TS90% (% sleep time in which SpO2 < 90%). Apnea was defined as decrements in airflow ≥ 90% from baseline for ≥ 10s, measured using an oronasal thermal sensor. Hypopnea was defined as a ≥ 30% decrease in flow lasting ≥ 10s associated with ≥ 4% oxyhemoglobin desaturation, or as a ≥ 50% decrease in flow lasting ≥ 10s associated with ≥ 3% oxygen desaturation. Participants were classified according to their AHI levels as non-OSA (AHI < 5/h), mild-moderate OSA (AHI from 5–30/h), or severe OSA (AHI > 30/h).

Huang J., Chen L., Li X., Chen M., Lin T, & Chen G. (2023). Association Between Metabolic-Associated Fatty Liver Disease and Obstructive Sleep Apnea: A Cross-Sectional Study. Nature and Science of Sleep, 15, 49-57.

Publication 2023

Apnea Arteries Electrocardiography Electroencephalography Electromyography Electrooculograms Movement Nose Oximetry, Pulse Oxygen Oxyhemoglobin Pharmaceutical Preparations Polysomnography Saturation of Peripheral Oxygen Sleep Sleep Apnea Syndromes Tibial Muscle, Anterior

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What are the different types or variations of Electrooculograms (EOGs)?

Electrooculograms (EOGs) can be categorized into several variations based on the specific application and measurement techniques: 1. Horizontal EOG (hEOG): Measures horizontal eye movements by placing electrodes on the outer corners of the eyes. 2. Vertical EOG (vEOG): Measures vertical eye movements by placing electrodes above and below the eyes. 3. Binocular EOG (bEOG): Records eye movements from both eyes simultaneously, providing insights into binocular coordination. 4. Scleral EOG: Uses specialized contact lenses or scleral attachments to measure eye position more precisely. 5. Electro-oculography (EOG): The broader term encompassing all techniques that measure the electrical potentials generated by eye movements.

What are the common applications of Electrooculograms (EOGs)?

Electrooculograms (EOGs) have a wide range of applications in research and clinical settings: 1. Eye movement analysis: EOGs are used to study saccadic eye movements, smooth pursuit, and other oculomotor functions. 2. Sleep analysis: EOGs can help detect and monitor rapid eye movement (REM) sleep, which is important for understanding sleep disorders. 3. Human-computer interaction: EOGs can be used as a control input for assistive technologies, allowing individuals with limited mobility to interact with computers and devices. 4. Neuroscience research: EOGs provide insights into visual processing and the neural mechanisms underlying eye movements, contributing to the understanding of the visual system. 5. Clinical diagnostics: EOGs can be used to assess neurological conditions, such as nystagmus, ophthalmoplegia, and vestibular disorders.

What are some common challenges in using Electrooculograms (EOGs)?

While Electrooculograms (EOGs) are a valuable tool, there are a few common challenges that researchers and clinicians may face: 1. Signal interference: EOG signals can be affected by factors like muscle activity, blink artifacts, and environmental noise, which may require careful signal processing and filtering. 2. Calibration and standardization: Accurate EOG measurements require proper calibration of the electrodes and standardized protocols, which can vary across different studies and laboratories. 3. Interpretation of results: Interpreting EOG data can be complex, as the electrical signals may not always directly correspond to the observed eye movements or visual processing. 4. Sensitivity to head movements: EOG recordings can be sensitive to head movements, which may limit their use in certain applications or require additional measures to compensate for this.

How can PubCompare.ai assist in optimizing Electrooculogram (EOG) research?

PubCompare.ai's innovative AI-driven platform can help optimize your Electrooculogram (EOG) research in several ways: 1. Efficient protocol screening: PubCompare.ai allows you to screen protocol literature more efficiently, helping you locate relevant EOG protocols from a vast array of publications, preprints, and patents. 2. AI-powered comparisons: The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to identify the most accurate and reproducible methods for your specific research goals. 3. Insights for reproducibility: By leveraging AI-powered comparisons, PubCompare.ai can help you choose the best EOG protocols to ensure reproducibility and accuracy in your research. 4. Tailored solutions: PubCompare.ai's suite of tools and insights can be customized to your unique EOG research needs, taking your work to new heights and unlocking new possibilities.

More about "Electrooculograms"

Electrooculograms (EOGs), also known as oculography, are a non-invasive technique used to measure eye movements and position.
This method records the electrical potentials generated by the movement of the eyes, providing valuable insights into visual processing and oculomotor control.
EOGs are widely utilized in various research and clinical applications, such as studying eye-related disorders, sleep analysis, and human-computer interaction.
The ActiveTwo system, ActiCAP, Alice 5, BrainAmp DC amplifier, BrainAmp, Brain Vision Recorder software, BrainAmp amplifier, and SynAmps2 from Neuroscan are some of the tools and software used in EOG research and analysis.
These systems and instruments allow for accurate recording, processing, and interpretation of EOG data, enabling researchers and clinicians to gain a deeper understanding of eye-related phenomena.
PubCompare.ai's innovative AI-driven platform can optimize your EOG research by helping you locate relevant protocols from literature, preprints, and patents, and leveraging AI-powered comparisons to identify the most accurate and reproducible methods.
This platform can be a valuable tool in unlocking new heights in your EOG research, providing you with the necessary insights and resources to advance your understanding of visual processing and oculomotor control.
Whether you're studying eye-related disorders, investigating sleep patterns, or exploring human-computer interaction, PubCompare.ai's suite of tools and insights can help you take your EOG research to new levels of excellence.
Embrace the power of this cutting-edge technology and discover the full potential of your electrooculogram studies.