Bibliografías temáticas / Scalp-EEG

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Literatura académica sobre el tema "Scalp-EEG"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 27 de julio de 2025

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Artículos de revistas sobre el tema "Scalp-EEG"

Johnson, Rashad, Grace Rizk, Harleen Kaur, Henry Ibekwe, Monica Atta, and Isis Gayed. "Refractory seizures: Prediction of outcome of surgical intervention based on results from PET-CT, PET-MRI and electroencephaolography." Neuroradiology Journal 33, no. 1 (2019): 57–65. http://dx.doi.org/10.1177/1971400919881464.

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Purpose The purpose of this article is to evaluate the effectiveness of fluorodeoxyglucose (FDG) positron emission tomography (PET)-computed tomography (CT) and PET-magnetic resonance imaging (MRI) with scalp and intracranial electroencephalogram (EEG) in predicting surgical outcomes in patients with refractory seizures. Methods Patients who underwent PET-CT and MRI fusion, scalp and intracranial EEG, and subsequent surgical intervention were retrospectively included. PET-CT were fused with MRI and interpreted by an experienced blinded reader. An area of hypometabolism on PET was identified as

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Choi, Jaehoon, Netiwit Kaongoen, HyoSeon Choi, Minuk Kim, Byung Hyung Kim, and Sungho Jo. "Decoding auditory-evoked response in affective states using wearable around-ear EEG system." Biomedical Physics & Engineering Express 9, no. 5 (2023): 055029. http://dx.doi.org/10.1088/2057-1976/acf137.

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Abstract Objective. In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli. Approach. EEG recorded from around the ears is compared to EEG collected according to the international 10–20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six

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Weisdorf, Sigge, Sirin W. Gangstad, Jonas Duun-Henriksen, Karina S. S. Mosholt, and Troels W. Kjær. "High similarity between EEG from subcutaneous and proximate scalp electrodes in patients with temporal lobe epilepsy." Journal of Neurophysiology 120, no. 3 (2018): 1451–60. http://dx.doi.org/10.1152/jn.00320.2018.

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Subcutaneous recording using electroencephalography (EEG) has the potential to enable ultra-long-term epilepsy monitoring in real-life conditions because it allows the patient increased mobility and discreteness. This study is the first to compare physiological and epileptiform EEG signals from subcutaneous and scalp EEG recordings in epilepsy patients. Four patients with probable or definite temporal lobe epilepsy were monitored with simultaneous scalp and subcutaneous EEG recordings. EEG recordings were compared by correlation and time-frequency analysis across an array of clinically relevan

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O'Neill, Nora S., Zoltan J. Koles, and Manouchehr Javidan. "Identification of the Temporal Components of Seizure Onset in the Scalp EEG." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 28, no. 3 (2001): 245–53. http://dx.doi.org/10.1017/s0317167100001402.

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Background:The identification of the earliest indication of rhythmical oscillations and paroxysmal events associated with an epileptic seizure is paramount in identifying the location of the seizure onset in the scalp EEG. In this work, data-dependent filters are designed that can help reveal obscure activity at the onset of seizures in problematic EEGs.Methods:Data-dependent filters were designed using temporal patterns common to selected segments from pre-ictal and ictal portions of the scalp EEG. Temporal patterns that accounted for more variance in the ictal segment than in the pre-ictal s

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Tanaka, Hideaki, Jean Gotman, Hui Ming Khoo, André Olivier, Jeffery Hall, and François Dubeau. "Neurophysiological seizure-onset predictors of epilepsy surgery outcome: a multivariable analysis." Journal of Neurosurgery 133, no. 6 (2020): 1863–72. http://dx.doi.org/10.3171/2019.9.jns19527.

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OBJECTIVEThe authors sought to determine which neurophysiological seizure-onset features seen during scalp electroencephalography (EEG) and intracerebral EEG (iEEG) monitoring are predictors of postoperative outcome in a large series of patients with drug-resistant focal epilepsy who underwent resective surgery.METHODSThe authors retrospectively analyzed the records of 75 consecutive patients with focal epilepsy, who first underwent scalp EEG and then iEEG (stereo-EEG) for presurgical assessment and who went on to undergo resective surgery between 2004 and 2015. To determine the independent pr

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Kawana, Takumi, Yuri Yoshida, Yuta Kudo, Chiho Iwatani, and Norihisa Miki. "Design and Characterization of an EEG-Hat for Reliable EEG Measurements." Micromachines 11, no. 7 (2020): 635. http://dx.doi.org/10.3390/mi11070635.

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In this study, a new hat-type electroencephalogram (EEG) device with candle-like microneedle electrodes (CMEs), called an EEG-Hat, was designed and fabricated. CMEs are dry EEG electrodes that can measure high-quality EEG signals without skin treatment or conductive gels. One of the challenges in the measurement of high-quality EEG signals is the fixation of electrodes to the skin, i.e., the design of a good EEG headset. The CMEs were able to achieve good contact with the scalp for heads of different sizes and shapes, and the EEG-Hat has a shutter mechanism to separate the hair and ensure good

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Ramantani, Georgia, Louis Maillard, and Laurent Koessler. "Correlation of invasive EEG and scalp EEG." Seizure 41 (October 2016): 196–200. http://dx.doi.org/10.1016/j.seizure.2016.05.018.

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Ye, Huanpeng, Guangye Li, Xinjun Sheng, and Xiangyang Zhu. "Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task." Journal of Neural Engineering 19, no. 2 (2022): 026043. http://dx.doi.org/10.1088/1741-2552/ac63e9.

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Abstract Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG. Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal wor

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Hua, Haoqiang, Wei Tang, Xiangmin Xu, David Dagan Feng, and Lin Shu. "Flexible Multi-Layer Semi-Dry Electrode for Scalp EEG Measurements at Hairy Sites." Micromachines 10, no. 8 (2019): 518. http://dx.doi.org/10.3390/mi10080518.

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One of the major challenges of daily wearable electroencephalogram (EEG) monitoring is that there are rarely suitable EEG electrodes for hairy sites. Wet electrodes require conductive gels, which will dry over the acquisition time, making them unstable for long-term EEG monitoring. Additionally, the electrode–scalp impedances of most dry electrodes are not adequate for high quality EEG collection at hairy sites. In view of the above problems, a flexible multi-layer semi-dry electrode was proposed for EEG monitoring in this study. The semi-dry electrode contains a flexible electrode body layer,

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Tao, James X., Amit Ray, Susan Hawes-Ebersole, and John S. Ebersole. "Intracranial EEG Substrates of Scalp EEG Interictal Spikes." Epilepsia 46, no. 5 (2005): 669–76. http://dx.doi.org/10.1111/j.1528-1167.2005.11404.x.

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Tesis sobre el tema "Scalp-EEG"

Jing, Min. "Predictability of epileptic seizures by fusion of scalp EEG and fMRI." Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/54740/.

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The systems for prediction of epileptic seizure investigated in recent years mainly rely on the traditional nonlinear analysis of the brain signals from intracranial electroencephalograph (EEG) recordings. The overall objective of this work focuses on investigation of the predictability of seizure from the scalp signals by applying effective blind source separation (BSS) techniques to scalp EEGs, in which the epileptic seizures are considered as independent components of the scalp EEGs. The ultimate goal of the work is to pave the way for epileptic seizure prediction from the scalp EEG. The ma

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Shahidi, Zandi Ali. "Scalp EEG quantitative analysis : automated real-time detection and prediction of epileptic seizures." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42748.

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As a chronic neurological disorder, epilepsy is associated with recurrent, unprovoked epileptic seizures resulting from a sudden disturbance of brain function. Long-term monitoring of epileptic patients' Electroencephalogram (EEG) is often needed for diagnosis of seizures, which is tedious, expensive, and time-consuming. Also, clinical staff may not identify the seizure early enough to determine the semiology at the onset. This motivates EEG-based automated real-time detection of seizures. Apart from their possible severe side effects, common treatments for epilepsy (medication and surgery) fa

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Chaudhary, U. J. "Haemodynamic correlates of interictal and ictal epileptic discharges and ictal semiology using simultaneous scalp video-EEG-fMRI and intracranial EEG-fMRI." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1388462/.

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Interictal and ictal epileptic discharges are produced by focal and widespread dysfunctional neuronal networks. Identification and characterization of epileptic discharges underlie the diagnosis and the choice of treatment for epilepsy patients. A better knowledge of the generation, propagation and localisation of epileptic discharges, and their interaction with the physiological and pathological brain networks can be very helpful in planning epilepsy surgery and minimizing the risk of damaging the physiological brain networks. This work describes a number of methodological developments and no

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Zelmann, Rina. "Classifying responses to imagined movements in scalp and intracranial EEG for a Brain Computer Interface." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=19274.

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Brain Computer Interfaces (BCI) are based on the analysis of electrical brain signals to produce an accurate identification of a subject's desired action. A 3-class Meta-classification BCI system that allows the classification of different movements or their imagination was developed. It is suitable for signals obtained by scalp and intracranial recordings. The classification was done in several steps. The first consisted in single-feature classification of each class against the others. This was followed by the selection

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Bayram, Mehmed Satuk Bugrahan. "Scalp EEG and TMS based Electrophysiological Study of Brain Function of Motor Control in Aging." Cleveland State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=csu1371688566.

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Martin, G. Neil. "Pre- and post-prandial human scalp-EEG response to olfactory stimulation, using the brain electrical activity mapping technique." Thesis, University of Warwick, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388639.

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Korats, Gundars. "Estimation de sources corticales : du montage laplacian aux solutions parcimonieuses." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0027/document.

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L’imagerie de source corticale joue un rôle important pour la compréhension fonctionnelle ou pathologique du cerveau. Elle permet d'estimer l'activation de certaines zones corticales en réponse à un stimulus cognitif donné et elle est également utile pour identifier la localisation des activités pathologiques, qui sont les premières étapes de l'étude des activations de réseaux neuronaux sous-jacents. Diverses méthodes d'investigation clinique peuvent être utilisées, des modalités d'imagerie (TEP, IRM) et magnéto-électroencéphalographie (EEG, SEEG, MEG). Nous souhaitions résoudre le problème à

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Onojima, Takayuki. "A study on the dynamical role of EEG phase for speech recognition." Kyoto University, 2018. http://hdl.handle.net/2433/232414.

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Koutek, Petr. "Lokalizace skalpových EEG elektrod ve strukturálních MRI datech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242130.

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The objective of this thesis is to design an algorithm used for localization of scalp electrodes in MRI structural data. The algorithm is based on fact that electrodes are visible on visualized head surface. The surface of a head is subdivided into smaller fragments, which are transformed from 3D space into 2D. The electrodes are then located in 2D space by use of registration techniques. The proposed algorithm is able to correctly locate up to 73% EEG electrodes, assuming that the subject has short hair. In case when a subject has long hair, the portion of correctly detected electrodes is 49%

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Korats, Gundars. "Estimation de sources corticales : du montage laplacian aux solutions parcimonieuses." Electronic Thesis or Diss., Université de Lorraine, 2016. http://www.theses.fr/2016LORR0027.

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Más fuentes

Libros sobre el tema "Scalp-EEG"

Baumgartner, Christoph. Clinical Electrophysiology of the Somatosensory Cortex: A Combined Study Using Electrocorticography, Scalp-EEG, and Magnetoencephalography. Springer Vienna, 1993.

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Baumgartner, Christoph. Clinical electrophysiology of the somatosensory cortex: A combined study using electrocorticography, scalp-EEG, and magnetoencephalography. Springer-Verlag, 1993.

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Michel, Christoph M., and Bin He. EEG Mapping and Source Imaging. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0045.

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This chapter describes methods to analyze the scalp electric field recorded with multichannel electroencephalography (EEG). With advances in high-density EEG, systems now allow fast and easy recording from 64 to 256 channels simultaneously. Pattern-recognition algorithms can characterize the topography of scalp electric fields and detect changes in topography over time and between experimental or clinical conditions. Methods for estimating the sources underlying the recorded scalp potential maps have increased the spatial resolution of EEG. The use of anatomical information in EEG source recon

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Krishnan, Vaishnav, Bernard S. Chang, and Donald L. Schomer. The Application of EEG to Epilepsy in Adults and the Elderly. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0019.

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Surface or scalp electroencephalography (EEG) has become an indispensable tool for the diagnosis, classification, and care of patients with epilepsy across the age spectrum. This chapter provides an overview of interictal and corresponding ictal scalp EEG patterns observed in adults with certain classical epilepsy syndromes. In patients with one or more new-onset seizures, the value of EEG testing begins with a close examination of the interictal record. The morphology, frequency, and topography of interictal epileptiform discharges (when present) are typically sufficient to broadly distinguis

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Baumgartner, Christoph. Clinical Electrophysiology of the Somatosensory Cortex: A Combined Study Using Electrocorticography, Scalp-EEG, and Magnetoencephalography. Springer, 2003.

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Wadman, Wytse J., and Fernando H. Lopes da Silva. Biophysical Aspects of EEG and MEG Generation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0004.

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This chapter reviews the essential physical principles involved in the generation of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. The general laws governing the electrophysiology of neuronal activity are analyzed within the formalism of the Maxwell equations that constitute the basis for understanding electromagnetic fields in general. Three main topics are discussed. The first is the forward problem: How can one calculate the electrical field that results from a known configuration of neuronal sources? The second is the inverse problem: Given an electrical field as

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Reed, Sean, Sonia Jego, and Antoine Adamantidis. Electroencephalography and Local Field Potentials in Animals. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0007.

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This chapter discusses the history, practice, and application of electroencephalography (EEG) and local field potential (LFP) recordings, with a particular focus on animal models. EEG measures the fluctuations of electrical activity resulting from ionic currents in the brain. These measurements are often taken from electrodes placed on the surface of the scalp, or in animal models, directly on the skull. LFP recordings are more invasive, measuring electrical current from all nearby dendritic synaptic activity within a volume of tissue. These two techniques are useful in determining how neural

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Amzica, Florin, and Fernando H. Lopes da Silva. Cellular Substrates of Brain Rhythms. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0002.

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The purpose of this chapter is to familiarize the reader with the basic electrical patterns of the electroencephalogram (EEG). Brain cells (mainly neurons and glia) are organized in multiple levels of intricate networks. The cellular membranes are semipermeable media between extracellular and intracellular solutions, populated by ions and other electrically charged molecules. This represents the basis of electrical currents flowing across cellular membranes, further generating electromagnetic fields that radiate to the scalp electrodes, which record changes in the activity of brain cells. This

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Gotman, Jean, and Nathan E. Crone. High-Frequency EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0033.

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Activities with frequencies between 60 and 80 Hz and approximately 500 Hz are labeled here as high-frequency activities. They were largely ignored until the beginning of the millennium, but their importance is now well recognized. They can be divided into activities occurring in the healthy brain in relation to sensory, motor, and cognitive or memory activity and activities occurring in the epileptic brain in the form of brief events (high-frequency oscillations), which appear to be an important marker of the brain regions that are able to generate seizures of focal origin. In humans, most of

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Pfurtscheller, Gert, and Fernando Lopes da Silva. EEG Event-Related Desynchronization and Event-Related Synchronization. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0040.

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Event-related desynchronization (ERD) reflects a decrease of oscillatory activity related to internally or externally paced events. The increase of rhythmic activity is called event-related synchronization (ERS). They represent dynamical states of thalamocortical networks associated with cortical information-processing changes. This chapter discusses differences between ERD/ERS and evoked response potentials and methodologies for quantifying ERD/ERS and selecting frequency bands. It covers the interpretation of ERD/ERS in the alpha and beta bands and theta ERS and alpha ERD in behavioral tasks

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Capítulos de libros sobre el tema "Scalp-EEG"

Tatum, William O. "Surgery Candidate (Scalp EEG)." In Epilepsy Case Studies. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01366-4_31.

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Yao, Dezhong. "Zero-Reference for Scalp EEG." In The Physics and Mathematics of Electroencephalogram. CRC Press, 2024. http://dx.doi.org/10.1201/9781032639260-13.

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Majumdar, Kaushik. "Neurophysiology of the Human Scalp EEG." In A Brief Survey of Quantitative EEG. CRC Press, 2017. http://dx.doi.org/10.1201/9781315117256-1.

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Relvas, Vânia, J. Miguel Sanches, and Patrícia Figueiredo. "Scalp EEG Continuous Space ERD/ERS Quantification." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_73.

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Baca, Christine B., and John M. Stern. "Scalp EEG in the epilepsy surgery evaluation." In The Treatment of Epilepsy. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118936979.ch57.

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Baumgartner, Christoph. "Spatiotemporal Modeling on ECoG, Scalp-EEG and MEG." In Clinical Electrophysiology of the Somatosensory Cortex. Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-6653-6_3.

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Ng, Kwun Kei, and Trevor B. Penney. "Probing Interval Timing with Scalp-Recorded Electroencephalography (EEG)." In Advances in Experimental Medicine and Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1782-2_11.

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Blanco, Susana A., Judith Creso, Alejandra Figliola, Rodrigo Quian Quiroga, and Osvaldo A. Rosso. "Nonlinear Dynamic Analysis of Scalp EEG Epileptic Signals." In Nonlinear Phenomena and Complex Systems. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2149-7_8.

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Sarnthein, Johannes, and Lukas Imbach. "What is the Relationship Between Scalp EEG, Intracranial EEG, and Microelectrode Activities?" In Studies in Neuroscience, Psychology and Behavioral Economics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20910-9_16.

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Pelliccia, Veronica, and Laura Tassi. "Role of Scalp and Invasive EEG in Epilepsy Surgery." In EEG: The First 100 Years. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86878-8_14.

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Actas de conferencias sobre el tema "Scalp-EEG"

Chandler, Alana, Amir Hossein Daraie, Patrick Myers, Jeff Craley, Sridevi Sarma, and Joon-Yi Kang. "Scalp EEG Correlates of Anti-Seizure Medications in Adult Epilepsy." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781905.

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Shen, Wenzhuo, Zhiyuan Yang, Guanglong Zhang, and Guixia Kang. "Exploring the Temporal Structure of Scalp EEG for Epilepsy Seizure Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821728.

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Hively, Lee M., and Nancy B. Munro. "SeizAlert: Seizure forewarning via scalp EEG." In Engineering Conference (BSEC): Exploring the Intersections of Interdisciplinary Biomedical Research. IEEE, 2009. http://dx.doi.org/10.1109/bsec.2009.5090475.

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Badier, Jean-Michel, Paul Gaillard, Patrick Marquis, and Patrick Chauvel. "Theoretical aproach of scalp EEG sampling." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5762251.

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Badier, Gaillard, Marquis, and Chauvel. "Theoretical Aproach of Scalp EEG Sampling." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.590552.

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Mohan, Nirmal, Muhammed Shanir P.P., Noufal Sulthan, Sofiya S., and Kashif Ahmad Khan. "Automatic Epileptic Seizure Prediction in Scalp EEG." In 2018 International Conference on Intelligent Circuits and Systems (ICICS). IEEE, 2018. http://dx.doi.org/10.1109/icics.2018.00063.

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Hively, Lee M., J. Todd McDonald, Nancy Munro, and Emily Cornelius. "Forewarning of epileptic events from scalp EEG." In 2013 Biomedical Sciences and Engineering Conference (BSEC). IEEE, 2013. http://dx.doi.org/10.1109/bsec.2013.6618498.

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Janwattanapong, Panuwat, Mercedes Cabrerizo, Hoda Rajaei, Alberto Pinzon-Ardila, Sergio Gonzalez-Arias, and Malek Adjouadi. "Epileptogenic brain connectivity patterns using scalp EEG." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7906024.

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Forrest Sheng Bao, Jue-Ming Gao, Jing Hu, Donald Lie, Yuanlin Zhang, and K. J. Oommen. "Automated epilepsy diagnosis using interictal scalp EEG." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332550.

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Krakovská, Anna, Zuzana Rošt’áková, Martina Chvosteková, and Jana Maslíková. "Do Scalp EEG Measurements Allow Causal Inference?" In 2023 14th International Conference on Measurement. IEEE, 2023. http://dx.doi.org/10.23919/measurement59122.2023.10164378.

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Informes sobre el tema "Scalp-EEG"

Madsen, Jens, Nikhil Kuppa, and Lucas Parra. The Brain, Body, and Behaviour Dataset - Neural Engineering Lab, CCNY. Fcp-indi, 2025. https://doi.org/10.15387/fcp_indi.retro.bbbd.

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When humans engage with video, their brain and body interact in response to sensory input. To investigate these interactions, we recorded and are releasing a dataset from N=178 participants across five experiments featuring short online educational videos. This dataset comprises approximately 110 hours of multimodal data including electrocardiogram (ECG), heart rate, respiration, breathing rate, pupil size, electrooculogram (EOG), gaze position, saccades, blinks, fixations, head movement, and electroencephalogram (EEG). Participants viewed 3-6 videos (mean total duration: 28±5 min) to test att

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