Auswahl der wissenschaftlichen Literatur zum Thema „High-Density Electroencephalogram (HD-EEG)“
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Zeitschriftenartikel zum Thema "High-Density Electroencephalogram (HD-EEG)"
Formica, Caterina, Simona De Salvo, Katia Micchìa, Fabio La Foresta, Serena Dattola, Nadia Mammone, Francesco Corallo et al. „Cortical Reorganization after Rehabilitation in a Patient with Conduction Aphasia Using High-Density EEG“. Applied Sciences 10, Nr. 15 (30.07.2020): 5281. http://dx.doi.org/10.3390/app10155281.
Der volle Inhalt der QuellePitetzis, Dimitrios, Christos Frantzidis, Elizabeth Psoma, Smaranda Nafsika Ketseridou, Georgia Deretzi, Anna Kalogera-Fountzila, Panagiotis D. Bamidis und Martha Spilioti. „The Pre-Interictal Network State in Idiopathic Generalized Epilepsies“. Brain Sciences 13, Nr. 12 (02.12.2023): 1671. http://dx.doi.org/10.3390/brainsci13121671.
Der volle Inhalt der QuelleFont-Clos, Francesc, Benedetta Spelta, Armando D’Agostino, Francesco Donati, Simone Sarasso, Maria Paola Canevini, Stefano Zapperi und Caterina A. M. La Porta. „Information Optimized Multilayer Network Representation of High Density Electroencephalogram Recordings“. Frontiers in Network Physiology 1 (28.09.2021). http://dx.doi.org/10.3389/fnetp.2021.746118.
Der volle Inhalt der QuelleAubonnet, Romain, Ovidiu C. Banea, Roberta Sirica, Eric M. Wassermann, Sahar Yassine, Deborah Jacob, Brynja Björk Magnúsdóttir et al. „P300 Analysis Using High-Density EEG to Decipher Neural Response to rTMS in Patients With Schizophrenia and Auditory Verbal Hallucinations“. Frontiers in Neuroscience 14 (20.11.2020). http://dx.doi.org/10.3389/fnins.2020.575538.
Der volle Inhalt der QuelleLuo, Huichun, Xiaojun Huang, Ziyi Li, Wotu Tian, Kan Fang1, Taotao Liu, Shige Wang et al. „An Electroencephalography Profile of Paroxysmal Kinesigenic Dyskinesia“. Advanced Science, 16.01.2024. http://dx.doi.org/10.1002/advs.202306321.
Der volle Inhalt der QuelleXiao, Songlin, Bin Shen, Chuyi Zhang, Xini Zhang, Suyong Yang, Junhong Zhou und Weijie Fu. „Anodal transcranial direct current stimulation enhances ankle force control and modulates the beta-band activity of the sensorimotor cortex“. Cerebral Cortex, 16.03.2023. http://dx.doi.org/10.1093/cercor/bhad070.
Der volle Inhalt der QuelleCaminiti, Silvia Paola, Sara Bernini, Sara Bottiroli, Micaela Mitolo, Riccardo Manca, Valentina Grillo, Micol Avenali et al. „Exploring the neural and behavioral correlates of cognitive telerehabilitation in mild cognitive impairment with three distinct approaches“. Frontiers in Aging Neuroscience 16 (27.06.2024). http://dx.doi.org/10.3389/fnagi.2024.1425784.
Der volle Inhalt der QuelleDissertationen zum Thema "High-Density Electroencephalogram (HD-EEG)"
Milon-Harnois, Gaëlle. „Détection automatique et analyse des oscillations à haute fréquence en EEG-HD de surface“. Electronic Thesis or Diss., Angers, 2023. http://www.theses.fr/2023ANGE0054.
Der volle Inhalt der QuelleConditions of a third of epileptics are not improved with current treatments, pushing doctors to consider surgery to remove the brain area generating seizures. High Frequency Oscillations (HFO) are emerging as a biomarker to localize these epileptogenic zones, but their detection is difficult due to their rarity and brevity. In scalp EEG the low amplitude of the signal complicates the task. This thesis aims to automate the detection of HFO in EEG-HD signals recorded at 1 KHz on 256 electrodes in 5 pediatric patients. After visual marking of HFO, classification models between HFO and background noise were explored. Signal processing knowledge has been exploited to extract features from time or frequency domain. The most statistically relevant features were selected and submitted to classic supervised algorithms (Logistic regression, random forest, MLP, gradient boosting). These methods were compared to deep algorithms (CNN, LSTM, Attention) automatically generating signal characteristics in the 1D time domain or those of 2D time-frequency maps. All models show convincing results, with the deep 1D algorithms being more efficient reaching 91% sensitivity and 87% specificity, outperforming previously published surface HFO detectors. Running the best models on the entire signal to automatically detect HFO showed promising results but this part of the work remains to be improved to overcome the HFO rarity in the data. Several lines of research are suggested