Literatura académica sobre el tema "Electrocorticography signals"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Electrocorticography signals".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Electrocorticography signals"
Miller, Kai J., Dora Hermes y Nathan P. Staff. "The current state of electrocorticography-based brain–computer interfaces". Neurosurgical Focus 49, n.º 1 (julio de 2020): E2. http://dx.doi.org/10.3171/2020.4.focus20185.
Texto completoReddy, Chandan G., Goutam G. Reddy, Hiroto Kawasaki, Hiroyuki Oya, Lee E. Miller y Matthew A. Howard. "Decoding movement-related cortical potentials from electrocorticography". Neurosurgical Focus 27, n.º 1 (julio de 2009): E11. http://dx.doi.org/10.3171/2009.4.focus0990.
Texto completoEnglert, Robert, Fabienne Rupp, Frank Kirchhoff, Klaus Peter Koch y Michael Schweigmann. "Technical characterization of an 8 or 16 channel recording system to acquire electrocorticograms of mice". Current Directions in Biomedical Engineering 3, n.º 2 (7 de septiembre de 2017): 595–98. http://dx.doi.org/10.1515/cdbme-2017-0124.
Texto completoYanagisawa, Takufumi, Masayuki Hirata, Youichi Saitoh, Tetsu Goto, Haruhiko Kishima, Ryohei Fukuma, Hiroshi Yokoi, Yukiyasu Kamitani y Toshiki Yoshimine. "Real-time control of a prosthetic hand using human electrocorticography signals". Journal of Neurosurgery 114, n.º 6 (junio de 2011): 1715–22. http://dx.doi.org/10.3171/2011.1.jns101421.
Texto completoRembado, Irene, Elisa Castagnola, Luca Turella, Tamara Ius, Riccardo Budai, Alberto Ansaldo, Gian Nicola Angotzi et al. "Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro-Electrocorticography". International Journal of Neural Systems 27, n.º 04 (10 de marzo de 2017): 1650052. http://dx.doi.org/10.1142/s0129065716500520.
Texto completoJeong, Ui-Jin, Jungpyo Lee, Namsun Chou, Kanghwan Kim, Hyogeun Shin, Uikyu Chae, Hyun-Yong Yu y Il-Joo Cho. "A minimally invasive flexible electrode array for simultaneous recording of ECoG signals from multiple brain regions". Lab on a Chip 21, n.º 12 (2021): 2383–97. http://dx.doi.org/10.1039/d1lc00117e.
Texto completoChen, Chao, Duk Shin, Hidenori Watanabe, Yasuhiko Nakanishi, Hiroyuki Kambara, Natsue Yoshimura, Atsushi Nambu, Tadashi Isa, Yukio Nishimura y Yasuharu Koike. "Prediction of Hand Trajectory from Electrocorticography Signals in Primary Motor Cortex". PLoS ONE 8, n.º 12 (27 de diciembre de 2013): e83534. http://dx.doi.org/10.1371/journal.pone.0083534.
Texto completoScherer, Reinhold, Stavros P. Zanos, Kai J. Miller, Rajesh P. N. Rao y Jeffrey G. Ojemann. "Classification of contralateral and ipsilateral finger movements for electrocorticographic brain-computer interfaces". Neurosurgical Focus 27, n.º 1 (julio de 2009): E12. http://dx.doi.org/10.3171/2009.4.focus0981.
Texto completoDelfino, Emanuela, Aldo Pastore, Elena Zucchini, Maria Francisca Porto Cruz, Tamara Ius, Maria Vomero, Alessandro D’Ausilio et al. "Prediction of Speech Onset by Micro-Electrocorticography of the Human Brain". International Journal of Neural Systems 31, n.º 07 (14 de junio de 2021): 2150025. http://dx.doi.org/10.1142/s0129065721500258.
Texto completoSlutzky, Marc W. y Robert D. Flint. "Physiological properties of brain-machine interface input signals". Journal of Neurophysiology 118, n.º 2 (1 de agosto de 2017): 1329–43. http://dx.doi.org/10.1152/jn.00070.2017.
Texto completoTesis sobre el tema "Electrocorticography signals"
JUBIEN, Guillaume. "Decoding Electrocorticography Signals by Deep Learning for Brain-Computer Interface". Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243903.
Texto completoDELFINO, EMANUELA. "Exploring micro-Electrocorticographic Signals: from Animal Models to Humans". Doctoral thesis, Università degli studi di Ferrara, 2020. http://hdl.handle.net/11392/2488155.
Texto completoL’elettocorticografia (ECoG) è una tecnica di registrazione delle variazioni nell’attività nervosa utilizzata sia in clinica sia nell’ambito della ricerca scientifica, caratterizzata da un’alta risoluzione spazio-temporale. In studi recenti, sono state esplorate diverse strategie al fine di decodificare il linguaggio a partire da segnali elettrocorticografici, con risultati altamente promettenti. Tuttavia, molteplici aspetti della sintesi del linguaggio a partire dall’attività cerebrale devono essere migliorati prima di intraprendere la sfida dei trial clinici. Il primo aspetto che potrebbe essere migliorato riguarda la tollerabilità dell’impianto in cronico. L’utilizzo dell’ECoG standard costituisce un vantaggio nella decodifica del linguaggio, per la sua capacità di coprire diverse aree coinvolte nella produzione del linguaggio; tuttavia, questo aspetto è anche uno dei suoi più grandi svantaggi. Infatti, la procedura standard di impianto delle matrici ECoG richiede operazioni chirurgiche invasive, a causa della loro dimensione. Un nuovo approccio basato su matrici ad alta densità di micro-elettrodi posizionate direttamente sulla zona di interesse potrebbe massimizzare la specificità del segnale registrato. Inoltre, l’uso di materiali conformabili minimizzerebbe il rischio di danno alla corteccia e di morte neuronale per reazioni infiammatorie. Il secondo aspetto che potrebbe essere migliorato è dovuto alla necessità di progettare neuro-protesi per la codifica e la decodifica del linguaggio implicito a partire da segnali neurali. L’obiettivo di questi sistemi è di migliorare la qualità della vita di pazienti affetti da sindromi locked-in (LIS). In tali pazienti, la vocalizzazione è resa impossibile da gravi paralisi, nonostante la capacità di generare il linguaggio a livello corticale sia essere ancora intatta. Un miglioramento chiave nello sviluppo di neuroprotesi per il linguaggio consisterebbe nell’implementare un sistema di trigger per la decodifica del linguaggio, basato su segnali neurali registrati in aree coinvolte nella produzione. Una volta determinata la configurazione ottimale per la registrazione dei segnali in vivo su ratti Long Evans in termini di configurazioni elettriche di reference e ground, Single-Ended-Screw (senza reference), Differential o Single-Ended-Reference (con reference), è stato possibile traslare la conoscenza sull’uomo. La miglior configurazione validata in vivo è stata anche testata durante un esperimento condotto su un paziente sottoposto alla rimozione chirurgica di un glioma. Durante l’esperimento sono stati testati due dispositivi μECoG, appoggiati su un’area eloquente nota come speech arrest. Il soggetto durante l’esperimento ha eseguito un compito di denominazione di oggetti per un totale di trenta ripetizioni per dispositivo. Le features estratte dall’analisi tempo-frequenza in diverse bande sono state usate per addestrare un classificatore al riconoscimento della fase preparatoria del linguaggio. I segnali registrati mostrano pattern temporalmente specifici nelle diverse bande di frequenza di interesse (15-30 Hz, 30-60 Hz, 70-150 Hz). In particolare, l’attività anticipatoria nella banda del gamma alto ha permesso di predire con elevata accuratezza le fasi preparatorie del linguaggio. Tale scoperta potrebbe essere integrata in neuro-protesi per il linguaggio, come trigger per iniziare la decodifica quando non è possibile misurare un evento esplicito. I risultati di questa tesi forniscono nuove prospettive per la comprensione di processi complessi e ancora poco chiari come la produzione del linguaggio.
Manoochehri, Mana. "Enregistrement simultané par EEG haute résolution et signal optique rapide (fast NIRS) chez l'enfant épileptique". Thesis, Amiens, 2017. http://www.theses.fr/2017AMIE0034.
Texto completoInterictal epileptic spikes (IES) represent a signature of the transient synchronous and excessive discharge of a large ensemble of cortical heterogeneous neurons and are widely accepted diagnostically as a signature of an epileptic underlying network. In this study, changes on neural configuration were observed in an animal and human models during the IES. For the first time, these changes were detected using Fast Optical Spectroscopy (FOS), which correspond to variations of scattered light from neural tissue during activation. These chages [i.e. changes] are associated with cellular mechanisms rather than hemodynamic responses with high spatial and temporal resolution. To investigate IES mechanism, a multimodal simultaneous analysis of the fast optical (FOS) and electrical (EEG/ECoG: time and frequency domain) changes was developed in both animal (15 rats) and human models (frontal IES, 3 children). To independently evaluate our methods, a control somatosensory evoked potential and optical response was designed in both animal and human models (5 healthy volunteers). The results suggest a relationship between (de)synchronization and optical changes whatever the epileptic model. This also proposed that changes in the fast optical signal which reflect changes in membrane configuration, are associated with the complex perturbations of the neuronal activation of the epileptic networks. We demonstrated that this non-invasive multiscale multimodal approach (FOS, ECoG/EEG) is suitable to study the pathophysiology of the IES in patients and shed new light on the mechanisms that propel neurons to the hypersynchronization in both animal and human epileptic models
Yelisyeyev, Andriy. "Interface cerveau-machine à partir d'enregistrement électrique cortical". Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS038/document.
Texto completoBrain Computer Interface (BCI) is a system for translation of brain neural activity into commands for external devices. This study was undertaken as a step toward the fully autonomous (self-paced) BCI functioning in natural environment which is of crucial importance for BCI clinical applications. To model the natural environment binary self-paced BCI experiments were carried out in freely moving animals. In comparison to the previous works, the long-term experimental sessions were carried out, which better comply with the real-life applications requirements. The main goal of the study was to discriminate the specific neuronal pattern related to the animal's control action against background brain activity of freely-moving animal. To achieve the necessary level of selectivity the Multi-Way Analysis was chosen since it provides a simultaneous signal processing in several domains, namely, temporal, frequency and spatial. To improve the capacity of the generic Multy-Way PLS approach for treatment of high-dimensional data, the Iterative NPLS algorithm is introduced in the current study. Having lower memory requirements it provides huge datasets treatment, allows high resolution, preserves the accuracy of the generic algorithm, and demonstrates better robustness. For adaptive calibration of BCI system the Recursive NPLS algorithm is proposed. Finally, the Penalized NPLS algorithm is developed for effective selection of feature subsets, namely, for subset of electrodes. The proposed algorithms were tested on artificial and real datasets. They demonstrated performance which either suppress or is comparable with one of the generic NPLS algorithm. Their computational efficiency is acceptable for the real-time applications. Developed algorithms were applied for calibration of the BCI system and were used in the real-time close-loop binary BCI experiments in animals. The proposed methods represent a prospective approach for further development of a human BCI system
Kuzdeba, Scott. "Characteristic time courses of electrocorticographic signals during speech". Thesis, 2019. https://hdl.handle.net/2144/38584.
Texto completoLibros sobre el tema "Electrocorticography signals"
Nuwer, Marc R. y Stephan Schuele. Electrocorticography. Editado por Donald L. Schomer y Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0030.
Texto completoCapítulos de libros sobre el tema "Electrocorticography signals"
Kanas, V. G., I. Mporas, H. L. Benz, N. Huang, N. V. Thakor, K. Sgarbas, A. Bezerianos y N. E. Crone. "Voice Activity Detection from Electrocorticographic Signals". En IFMBE Proceedings, 1643–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_405.
Texto completoSinkin, M. V., K. V. Volkova, M. S. Kondratova, A. M. Voskoboynikov, M. A. Lebedev, M. D. Ivanova y A. E. Ossadtchi. "Passive Intraoperative Language Mapping Using Electrocorticographic Signals". En Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics, 533–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71637-0_61.
Texto completoMatsumoto, Riki y Takeharu Kunieda. "Cortico-Cortical Evoked Potential Mapping". En Invasive Studies of the Human Epileptic Brain, editado por Samden D. Lhatoo, Philippe Kahane y Hans O. Lüders, 431–52. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198714668.003.0032.
Texto completoActas de conferencias sobre el tema "Electrocorticography signals"
Kurnaz, Ismail y Erdem Erkan. "Classification of Electrocorticography signals reduced by Wavelet Transform". En 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495752.
Texto completoDate, Hiroto, Keisuke Kawasaki, Isao Hasegawa y Takayuki Okatani. "Deep Learning for Natural Image Reconstruction from Electrocorticography Signals". En 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983029.
Texto completoFukuma, Ryohei, Takufumi Yanagisawa, Shinji Nishimoto, Masataka Tanaka, Shota Yamamoto, Satoru Oshino, Yukiyasu Kamitani y Haruhiko Kishima. "Decoding Visual Stimulus in Semantic Space from Electrocorticography Signals". En 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00027.
Texto completoKaplan, Alan D., Qi Cheng, Piyush Karande, Elizabeth Tran, Maryam Bijanzadeh, Heather Dawes y Edward Chang. "Localization of Emotional Affect in Electrocorticography Using a Model Based Discrimination Measure". En 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048944.
Texto completoKrishnan, Jithin, R. Rethnagireeshwar, Biju Benjamin, Nitha V. Panicker y A. R. Biju Ramu. "High precision resistance spot welding with subdural electrodes for acute electrocorticography applications". En 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). IEEE, 2017. http://dx.doi.org/10.1109/icpcsi.2017.8391916.
Texto completoChin, Cesar Marquez, Milos R. Popovic, Tracy Cameron, Andres Lozano y Robert Chen. "Identification of Arm Movements Using Electrocorticographic Signals". En 2007 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE, 2007. http://dx.doi.org/10.1109/cne.2007.369645.
Texto completoWei Wang, A. D. Degenhart, G. P. Sudre, D. A. Pomerleau y E. C. Tyler-Kabara. "Decoding semantic information from human electrocorticographic (ECoG) signals". En 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091553.
Texto completoMishra, Apurva, Fan Zhang y Brian P. Otis. "ElectroCorticoGraphy (ECoG) acquisition exploiting signal characteristics for reduced power". En 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2011. http://dx.doi.org/10.1109/biocas.2011.6107721.
Texto completoGruenwald, J., C. Kapeller, K. Kamada, J. Scharinger y C. Guger. "Unsupervised Quantification of High-Gamma Activity in Electrocorticographic Signals". En 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441139.
Texto completoZhao, Rui, Gerwin Schalk y Qiang Ji. "Coupled Hidden Markov Model for Electrocorticographic Signal Classification". En 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.325.
Texto completo