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Artículos de revistas sobre el tema "Scalp-EEG"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 27 de julio de 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Paredes-Aragón, EM, M. Chávez-Castillo, GL Barkley, JG Burneo, and A. Suller-Martí. "P.129 Bi-insular Responsive Neurostimulation Artifact on Scalp Electroencephalogram." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 48, s3 (2021): S56. http://dx.doi.org/10.1017/cjn.2021.405.

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Background: Background: Responsive Neurostimulation (RNS) has proven efficacy in treating medically resistant epilepsy as an intracranial system detecting, recording and treating seizures automatically. No information exists pertaining to artifact characteristics of RNS findings in scalp EEG. Methods: A 30 year-old female was diagnosed using intracranial electroencephalography(iEEG), with bi-insular epilepsy, of unknown cause. She presented large number of focal unaware non-motor seizures and seizures with progression to bilateral tonic-clonic. She was implanted with bi-insular RNS. Results: D

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12

Baumgartner, Christoph, and Johannes P. Koren. "Seizure detection using scalp-EEG." Epilepsia 59 (June 2018): 14–22. http://dx.doi.org/10.1111/epi.14052.

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13

Selvam, V. Salai, and S. Shenbaga Devi. "Analysis of Spectral Features of EEG signal in Brain Tumor Condition." Measurement Science Review 15, no. 4 (2015): 219–25. http://dx.doi.org/10.1515/msr-2015-0030.

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AbstractThe scalp electroencephalography (EEG) signal is an important clinical tool for the diagnosis of several brain disorders. The objective of the presented work is to analyze the feasibility of the spectral features extracted from the scalp EEG signals in detecting brain tumors. A set of 16 candidate features from frequency domain is considered. The significance on the mean values of these features between 100 brain tumor patients and 102 normal subjects is statistically evaluated. Nine of the candidate features significantly discriminate the brain tumor case from the normal one. The resu

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14

Hawsawi, Hassan B., Anastasia Papadaki, Vejay N. Vakharia, et al. "Safety of Simultaneous Scalp and Intracranial EEG and fMRI: Evaluation of RF-Induced Heating." Bioengineering 12, no. 6 (2025): 564. https://doi.org/10.3390/bioengineering12060564.

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The acquisition of electroencephalography (EEG) concurrently with functional magnetic resonance imaging (fMRI) requires a careful consideration of the health hazards resulting from interactions between the scanner’s electromagnetic fields and EEG recording equipment. The primary safety concern is excessive RF-induced heating of the tissue in the vicinity of electrodes. We have previously demonstrated that concurrent intracranial EEG (icEEG) and fMRI data acquisitions (icEEG-fMRI) can be performed with acceptable risk in specific conditions using a head RF transmit coil. Here, we estimate the p

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15

Höller, Peter, Eugen Trinka, and Yvonne Höller. "High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence." Computational Intelligence and Neuroscience 2018 (August 7, 2018): 1–9. http://dx.doi.org/10.1155/2018/1638097.

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High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scal

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16

Wang, Gang, and Doutian Ren. "Effect of Brain-to-Skull Conductivity Ratio on EEG Source Localization Accuracy." BioMed Research International 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/459346.

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The goal of this study was to investigate the influence of the brain-to-skull conductivity ratio (BSCR) on EEG source localization accuracy. In this study, we evaluated four BSCRs: 15, 20, 25, and 80, which were mainly discussed according to the literature. The scalp EEG signals were generated by BSCR-related forward computation for each cortical dipole source. Then, for each scalp EEG measurement, the source reconstruction was performed to identify the estimated dipole sources by the actual BSCR and the misspecified BSCRs. The estimated dipole sources were compared with the simulated dipole s

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17

Fujimoto, Ayataka, Yuji Matsumaru, Yosuke Masuda, et al. "Endovascular Electroencephalogram Records Simultaneous Subdural Electrode-Detectable, Scalp Electrode-Undetectable Interictal Epileptiform Discharges." Brain Sciences 12, no. 3 (2022): 309. http://dx.doi.org/10.3390/brainsci12030309.

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Introduction: We hypothesized that an endovascular electroencephalogram (eEEG) can detect subdural electrode (SDE)-detectable, scalp EEG-undetectable epileptiform discharges. The purpose of this study is, therefore, to measure SDE-detectable, scalp EEG-undetectable epileptiform discharges by an eEEG on a pig. Methods: A pig under general anesthesia was utilized to measure an artificially generated epileptic field by an eEEG that was able to be detected by an SDE, but not a scalp EEG as a primary outcome. We also compared the phase lag of each epileptiform discharge that was detected by the eEE

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18

Kang, Byeong-Cheol, and Tae-Jun Ha. "Noninvasive electroencephalogram sensors based on all-solution-processed trapezoidal electrode array." Applied Physics Letters 120, no. 21 (2022): 213301. http://dx.doi.org/10.1063/5.0087848.

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Conventional wet electrodes, such as a silver/silver chloride electrode, are limited for electroencephalogram (EEG) sensors directly attached to the scalp with existing hair due to their incomplete contact and increased impedance. In this study, an all-solution-processed trapezoidal electrode array is demonstrated for highly sensitive and reliable detection of EEG signals even when in direct contact with the scalp. The proposed noninvasive EEG sensors based on nanocomposites consisting of single-wall carbon nanotube random networks incorporated into a gelatin matrix exhibited a relatively low

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19

Hemmsen, Martin, Kaare Mikkelsen, Mike Rank, and Preben Kidmose. "272 Long-term monitoring of trait-like characteristics of the sleep electroencephalogram using ear-EEG." Sleep 44, Supplement_2 (2021): A109. http://dx.doi.org/10.1093/sleep/zsab072.271.

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Abstract Introduction Wearable electroencephalogram (EEG) monitoring has a remarkable potential, it is safe, scalable and can track neural signatures for long periods. One such signature is the power spectra of non-rapid-eye-movement (NREM) sleep which has been shown to demonstrate a trait-like characteristic. Changes in personalized signatures has been associated with biomarkers of Alzheimer’s disease and is of great interest for early detection and clinical management. This work investigates monitoring of signatures using a wearable device that records EEG from the ear (ear-EEG) and compares

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20

Pflieger, Mark E. "Inferring contextual field interactions from scalp EEG." Behavioral and Brain Sciences 26, no. 1 (2003): 99–100. http://dx.doi.org/10.1017/s0140525x03390028.

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AbstractThis commentary highlights methods for using scalp EEG to make inferences about contextual field interactions, which, in view of the target article, may be specially relevant to the study of schizophrenia. Although scalp EEG has limited spatial resolution, prior knowledge combined with experimental manipulations may be used to strengthen inferences about underlying brain processes. Both spatial and temporal context are discussed within the framework of nonlinear interactions. Finally, results from a visual contour integration EEG pilot study are summarized in view of a hypothesis that

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21

Fallah, Merrick S., Binod Neupane, Mutaz Al Nassar, Rajesh RamachandranNair, and Kevin C. Jones. "Interpreting the Tests of Focal Cortical Dysplasia for Epilepsy Surgery Referral." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 46, no. 5 (2019): 559–65. http://dx.doi.org/10.1017/cjn.2019.241.

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ABSTRACT:Objective: Focal cortical dysplasia (FCD) is a common cause of refractory, focal onset epilepsy in children. Interictal, scalp electroencephalograph (EEG) markers have been associated with these pathologies and epilepsy surgery may be an option for some patients. We aim to study how scalp EEG and magnetic resonance imaging (MRI) markers of FCD affect referral of these patients for surgical evaluation. Methods: A single-center, retrospective review of children with focal onset epilepsy. Patients were included if they were between 1 month and 18 years of age, had focal onset seizures, p

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22

Protopopescu, Vladimir A., Lee M. Hively, and Paul C. Gailey. "Epileptic Event Forewarning From Scalp EEG." Journal of Clinical Neurophysiology 18, no. 3 (2001): 223–45. http://dx.doi.org/10.1097/00004691-200105000-00003.

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23

Lee, Sang Kun. "WS8.2 Scalp and Intracranial Ictal EEG." Clinical Neurophysiology 120 (April 2009): S19. http://dx.doi.org/10.1016/s1388-2457(09)60051-1.

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24

Baborie, A., A. Hufnagel, M. Kurthen, and C. E. Elger. "Scalp-EEG Recording via Methohexital Narcosis." Journal of Epilepsy 11, no. 3 (1998): 136–43. http://dx.doi.org/10.1016/s0896-6974(97)00135-7.

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Ferree, T. C., M. T. Clay, and D. M. Tucker. "The spatial resolution of scalp EEG." Neurocomputing 38-40 (June 2001): 1209–16. http://dx.doi.org/10.1016/s0925-2312(01)00568-9.

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26

Rubega, Maria, Fabio Scarpa, Debora Teodori, Anne-Sophie Sejling, Christian S. Frandsen, and Giovanni Sparacino. "Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients." Entropy 22, no. 1 (2020): 81. http://dx.doi.org/10.3390/e22010081.

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Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic–hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi inde

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27

Moghavvemi, M., and S. Mehrkanoon. "DETECTION OF THE ONSET OF EPILEPTIC SEIZURE SIGNAL FROM SCALP EEG USING BLIND SIGNAL SEPARATION." Biomedical Engineering: Applications, Basis and Communications 21, no. 04 (2009): 287–90. http://dx.doi.org/10.4015/s1016237209001301.

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Investigation of epileptic electroencephalogram (EEG) signal is one of the major areas of study in the field of signal processing. The ability to detect the seizure signal and its origin within the brain is of prime importance. This paper proposes a sequential blind signal separation (BSS) based system to extract the seizure signal from scalp EEG and to pinpoint the main location of seizure signal within the brain. BSS algorithm is used to demix the EEG signal into signals with independent features. Scalp time-mapping process is applied to determine the main location of the extracted seizure s

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28

Mhapankar, Umesh, and Mr Milind Shah. "Mapping the Imagined Speech Location on the Brain Scalp Through Magnetoencephalography (MEG)." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (2022): 117–21. http://dx.doi.org/10.35940/ijrte.b7144.0711222.

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People with autism speech disorders, paralysis, or muteness cannot communicate via speech. These individuals can think but cannot express and create overt speech. As a result, the system must obtain and interpret the electric and magnetic signal developed at the Scalp during imagined or intended speech. These magnetic signals are termed MEG (Magnetoencephalography), and electrical signals are named EEG (Electroencephalography). This technology must be wearable, non-invasive, and easy to use daily. To make the system wearable, the location of the electrode is essential. Since the EEG has good t

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Caboclo, Luís Otávio Sales Ferreira, Eliana Garzon, Flávia Saori Miyashira, et al. "Temporal lobe epilepsy with unilateral hippocampal sclerosis and contralateral temporal scalp seizure onset: report of four patients with "burned-out hippocampus"." Journal of Epilepsy and Clinical Neurophysiology 11, no. 2 (2005): 79–86. http://dx.doi.org/10.1590/s1676-26492005000200003.

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ABSTRACT OBJECTIVES: Patients with temporal lobe epilepsy (TLE) and unilateral severe hippocampal sclerosis (HS) may have contralateral temporal scalp ictal onset. This has recently been called "burned-out hippocampus", which is believed to be a rare entity. In this study we report four patients with unilateral hippocampal sclerosis and contralateral ictal onset registered by scalp electrodes. We discuss the importance of such cases in presurgical evaluation of patients with TLE, as well as possible strategies used for evaluation of these particular cases. PATIENTS AND METHODS: We reviewed cha

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Umesh, Mhapankar, and Milind Shah Mr. "Mapping the Imagined Speech Location on the Brain Scalp Through Magnetoencephalography (MEG)." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (2022): 117–21. https://doi.org/10.35940/ijrte.B7144.0711222.

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<strong>Abstract:</strong> People with autism speech disorders, paralysis, or muteness cannot communicate via speech. These individuals can think but cannot express and create overt speech. As a result, the system must obtain and interpret the electric and magnetic signal developed at the Scalp during imagined or intended speech. These magnetic signals are termed MEG (Magnetoencephalography), and electrical signals are named EEG (Electroencephalography). This technology must be wearable, non-invasive, and easy to use daily. To make the system wearable, the location of the electrode is essentia

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31

Kotiuchyi, Ivan, Riccardo Pernice, Anton Popov, Luca Faes, and Volodymyr Kharytonov. "A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks." Brain Sciences 10, no. 9 (2020): 657. http://dx.doi.org/10.3390/brainsci10090657.

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This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistica

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32

Ye, Yan, Xiaoping Li, Tiecheng Wu, Zhe Li, and Wenwen Xie. "Material and physical model for evaluation of deep brain activity contribution to EEG recordings." Functional Materials Letters 08, no. 03 (2015): 1540003. http://dx.doi.org/10.1142/s1793604715400032.

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Deep brain activity is conventionally recorded with surgical implantation of electrodes. During the neurosurgery, brain tissue damage and the consequent side effects to patients are inevitably incurred. In order to eliminate undesired risks, we propose that deep brain activity should be measured using the noninvasive scalp electroencephalography (EEG) technique. However, the deeper the neuronal activity is located, the noisier the corresponding scalp EEG signals are. Thus, the present study aims to evaluate whether deep brain activity could be observed from EEG recordings. In the experiment, a

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Wang, Xiang Li, Shi Mei Su, and Zhi Gang Shang. "Model and Simulation of the Brain Scalp Potential Analysis." Applied Mechanics and Materials 198-199 (September 2012): 942–47. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.942.

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Based on spherical head models, this paper, by employing the finite element method (FEM), analyzes the potential distribution of the brain scalp surface and attempts to work out the electroencephalography (EEG) forward problem, in hope of finding out the impact the dipole parameters has on it. According to the electromagnetism theory, this paper discusses the general resolution of EEG, it requires electric potentials of the globe's surface, and graphically displays results of computation through finite element post-processing, which tests their effectiveness. Furthermore, it analyzes the influ

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Khoa, Truong Quang Dang, Nguyen Thi Minh Huong, and Vo Van Toi. "Detecting Epileptic Seizure from Scalp EEG Using Lyapunov Spectrum." Computational and Mathematical Methods in Medicine 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/847686.

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One of the inherent weaknesses of the EEG signal processing is noises and artifacts. To overcome it, some methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These methods reduced noises, but they were hazardous to patients. In this study, we propose using Lyapunov spectrum to filter noise and detect epilepsy on scalp EEG signals only. We determined that the Lyapunov spectrum can be considered as the most expected method to evaluate chaotic behavior of scalp EEG recordin

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Voytek, Bradley, Lavi Secundo, Aurelie Bidet-Caulet, et al. "Hemicraniectomy: A New Model for Human Electrophysiology with High Spatio-temporal Resolution." Journal of Cognitive Neuroscience 22, no. 11 (2010): 2491–502. http://dx.doi.org/10.1162/jocn.2009.21384.

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Human electrophysiological research is generally restricted to scalp EEG, magneto-encephalography, and intracranial electrophysiology. Here we examine a unique patient cohort that has undergone decompressive hemicraniectomy, a surgical procedure wherein a portion of the calvaria is removed for several months during which time the scalp overlies the brain without intervening bone. We quantify the differences in signals between electrodes over areas with no underlying skull and scalp EEG electrodes over the intact skull in the same subjects. Signals over the hemicraniectomy have enhanced amplitu

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Zakaria, Tarek, Katherine Noe, Elson So, et al. "Scalp and Intracranial EEG in Medically Intractable Extratemporal Epilepsy with Normal MRI." ISRN Neurology 2012 (June 25, 2012): 1–9. http://dx.doi.org/10.5402/2012/942849.

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Purpose. To investigate EEG and SPECT in the surgical outcome of patients with normal MRI (nonlesional) and extratemporal lobe epilepsy. Methods. We retrospectively identified 41 consecutive patients with nonlesional extratemporal epilepsy who underwent epilepsy surgery between 1997 and 2007. The history, noninvasive diagnostic studies (scalp EEG, MRI, and SPECT) and intracranial EEG (iEEG) monitoring was reviewed. Scalp and iEEG ictal onset patterns were defined. The association of preoperative studies and postoperative seizure freedom was analyzed using Kaplan-Meier analysis, log-rank test,

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Antoniades, Andreas, Loukianos Spyrou, David Martin-Lopez, et al. "Deep Neural Architectures for Mapping Scalp to Intracranial EEG." International Journal of Neural Systems 28, no. 08 (2018): 1850009. http://dx.doi.org/10.1142/s0129065718500090.

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Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly ma

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So, Rosa Q., Vibhor Krishna, Nicolas Kon Kam King, et al. "Prediction and detection of seizures from simultaneous thalamic and scalp electroencephalography recordings." Journal of Neurosurgery 126, no. 6 (2016): 2036–44. http://dx.doi.org/10.3171/2016.7.jns161282.

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OBJECTIVEThe authors explored the feasibility of seizure detection and prediction using signals recorded from the anterior thalamic nucleus, a major target for deep brain stimulation (DBS) in the treatment of epilepsy.METHODSUsing data from 5 patients (13 seizures in total), the authors performed a feasibility study and analyzed the performance of a seizure prediction and detection algorithm applied to simultaneously acquired scalp and thalamic electroencephalography (EEG). The thalamic signal was obtained from DBS electrodes. The applied algorithm used the similarity index as a nonlinear meas

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Shaw, Saurabh Bhaskar, Kiret Dhindsa, James P. Reilly, and Suzanna Becker. "Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics." Neural Computation 31, no. 11 (2019): 2177–211. http://dx.doi.org/10.1162/neco_a_01229.

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The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory,

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Kurmanavičiūtė, Dovilė, Hanna Kataja, and Lauri Parkkonen. "Comparing MEG and EEG measurement set-ups for a brain–computer interface based on selective auditory attention." PLOS ONE 20, no. 4 (2025): e0319328. https://doi.org/10.1371/journal.pone.0319328.

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Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and oﬄine classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we reco

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Büyükgöze, Selma. "NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY." International Conference on Technics, Technologies and Education, ICTTE 2019 (2019): 139–45. http://dx.doi.org/10.15547/ictte.2019.02.095.

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Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves t

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Liu, Junchen, Sen Lin, Wenzheng Li, et al. "Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes." Research 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.34133/2022/9830457.

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Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be

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Javidan, Manouchehr. "Electroencephalography in Mesial Temporal Lobe Epilepsy: A Review." Epilepsy Research and Treatment 2012 (June 17, 2012): 1–17. http://dx.doi.org/10.1155/2012/637430.

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Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data

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Iqbal, Sadaf, Muhammed Shanir P.P., Yusuf Uzzaman Khan, and Omar Farooq. "EEG Analysis of Imagined Speech." International Journal of Rough Sets and Data Analysis 3, no. 2 (2016): 32–44. http://dx.doi.org/10.4018/ijrsda.2016040103.

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Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. Wit

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Boran, Ece, Tommaso Fedele, Peter Klaver, et al. "Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load." Science Advances 5, no. 3 (2019): eaav3687. http://dx.doi.org/10.1126/sciadv.aav3687.

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The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. To investigate the influence of load on working memory, we asked human subjects to maintain sets of letters in memory while we recorded single neurons and intracranial encephalography (EEG) in the medial temporal lobe and scalp EEG. Along the periods of a trial, hippocampal neural firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects’ behavior during retrieval. During maintenan

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Boran, Ece, Tommaso Fedele, Peter Klaver, et al. "Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load." Science Advances 5, no. 3 (2019): 1–14. https://doi.org/10.1126/sciadv.aav3687.

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The maintenance of items in working memory relies on persistent neural activity in a widespread network of brain areas. To investigate the influence of load on working memory, we asked human subjects to maintain sets of letters in memory while we recorded single neurons and intracranial encephalography (EEG) in the medial temporal lobe and scalp EEG. Along the periods of a trial, hippocampal neural firing differentiated between success and error trials during stimulus encoding, predicted workload during memory maintenance, and predicted the subjects’ behavior during retrieval. During mai

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Zibrandtsen, I., P. Kidmose, M. Otto, J. Ibsen, and T. W. Kjaer. "Case comparison of sleep features from ear-EEG and scalp-EEG." Sleep Science 9, no. 2 (2016): 69–72. http://dx.doi.org/10.1016/j.slsci.2016.05.006.

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Worrell, Gregory. "High-Frequency Oscillations Recorded on Scalp EEG." Epilepsy Currents 12, no. 2 (2012): 57–58. http://dx.doi.org/10.5698/1535-7511-12.2.57.

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Quintero-Zea, Andres, Jose D. Lopez, Keith Smith, Natalia Trujillo, Mario A. Parra, and Javier Escudero. "Phenotyping Ex-Combatants From EEG Scalp Connectivity." IEEE Access 6 (2018): 55090–98. http://dx.doi.org/10.1109/access.2018.2872765.

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Dvorak, Dino, Andrea Shang, Samah Abdel-Baki, Wendy Suzuki, and Andre A. Fenton. "Cognitive Behavior Classification From Scalp EEG Signals." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 4 (2018): 729–39. http://dx.doi.org/10.1109/tnsre.2018.2797547.

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