Artículos de revistas sobre el tema "EEG DENOISING"
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An, Yang, Hak Keung Lam y Sai Ho Ling. "Auto-Denoising for EEG Signals Using Generative Adversarial Network". Sensors 22, n.º 5 (23 de febrero de 2022): 1750. http://dx.doi.org/10.3390/s22051750.
Texto completoElsherbieny, Zeinab, Nagy Messiha, Adel S. El-Fisawy, Mohamed Rihan y Fathi E. Abd El-Samie. "Efficient Denoising Schemes of EEG Signals". Menoufia Journal of Electronic Engineering Research 28, n.º 1 (1 de diciembre de 2019): 209–13. http://dx.doi.org/10.21608/mjeer.2019.77020.
Texto completoGrobbelaar, Maximilian, Souvik Phadikar, Ebrahim Ghaderpour, Aaron F. Struck, Nidul Sinha, Rajdeep Ghosh y Md Zaved Iqubal Ahmed. "A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform". Signals 3, n.º 3 (17 de agosto de 2022): 577–86. http://dx.doi.org/10.3390/signals3030035.
Texto completoZhao, Haoyan y Bin Guo. "EEG Signal Denoising Based on Deep Residual Shrinkage Network". Journal of Physics: Conference Series 2395, n.º 1 (1 de diciembre de 2022): 012076. http://dx.doi.org/10.1088/1742-6596/2395/1/012076.
Texto completoPERDHANA, HASBIAN FAUZY y HASBALLAH ZAKARIA. "Pembersihan Artefak EOG dari Sinyal EEG menggunakan Denoising Autoencoder". ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, n.º 3 (19 de julio de 2022): 639. http://dx.doi.org/10.26760/elkomika.v10i3.639.
Texto completoYan, Wenqiang, Chenghang Du, Yongcheng Wu, Xiaowei Zheng y Guanghua Xu. "SSVEP-EEG Denoising via Image Filtering Methods". IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 1634–43. http://dx.doi.org/10.1109/tnsre.2021.3104825.
Texto completoUTHAYAKUMAR, R. y D. EASWARAMOORTHY. "MULTIFRACTAL-WAVELET BASED DENOISING IN THE CLASSIFICATION OF HEALTHY AND EPILEPTIC EEG SIGNALS". Fluctuation and Noise Letters 11, n.º 04 (diciembre de 2012): 1250034. http://dx.doi.org/10.1142/s0219477512500344.
Texto completoZhang, Zhen, Xiaoyan Yu, Xianwei Rong y Makoto Iwata. "A Novel Multimodule Neural Network for EEG Denoising". IEEE Access 10 (2022): 49528–41. http://dx.doi.org/10.1109/access.2022.3173261.
Texto completoTurnip, Arjon y Jasman Pardede. "Artefacts Removal of EEG Signals with Wavelet Denoising". MATEC Web of Conferences 135 (2017): 00058. http://dx.doi.org/10.1051/matecconf/201713500058.
Texto completoLi, Junhua, Zbigniew Struzik, Liqing Zhang y Andrzej Cichocki. "Feature learning from incomplete EEG with denoising autoencoder". Neurocomputing 165 (octubre de 2015): 23–31. http://dx.doi.org/10.1016/j.neucom.2014.08.092.
Texto completoJAYALAXMI, ANEM y KUMAR G. SATEESH. "DENOISING OF EEG SIGNAL USING FrFT BASED BARLETT WINDOW". i-manager's Journal on Digital Signal Processing 5, n.º 1 (2017): 18. http://dx.doi.org/10.26634/jdp.5.1.13528.
Texto completoGeetha, G. y S. N. Geethalakshmi. "EEG Denoising using SURE thresholding based on Wavelet Transforms". International Journal of Computer Applications 24, n.º 6 (30 de junio de 2011): 29–33. http://dx.doi.org/10.5120/2948-3935.
Texto completoChu, Ruibo, Jian Wang, Qian Zhang y Huanhuan Chen. "An adaptive noise removal method for EEG signals". Journal of Physics: Conference Series 2414, n.º 1 (1 de diciembre de 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2414/1/012007.
Texto completoPhadikar, Souvik, Nidul Sinha, Rajdeep Ghosh y Ebrahim Ghaderpour. "Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter". Sensors 22, n.º 8 (12 de abril de 2022): 2948. http://dx.doi.org/10.3390/s22082948.
Texto completoSohaib, Muhammad, Ayesha Ghaffar, Jungpil Shin, Md Junayed Hasan y Muhammad Taseer Suleman. "Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence". International Journal of Environmental Research and Public Health 19, n.º 20 (14 de octubre de 2022): 13256. http://dx.doi.org/10.3390/ijerph192013256.
Texto completoNagar, Subham, Ahlad Kumar y M. N. S. Swamy. "Orthogonal features-based EEG signal denoising using fractionally compressed autoencoder". Signal Processing 188 (noviembre de 2021): 108225. http://dx.doi.org/10.1016/j.sigpro.2021.108225.
Texto completoN., PADMAJA, BHARATHI M. y SUJATHA E. "A GUI based EEG Signal Denoising using Hilbert Huang Transform". i-manager’s Journal on Electronics Engineering 7, n.º 1 (2016): 25. http://dx.doi.org/10.26634/jele.7.1.8281.
Texto completoAlbera, L., A. Kachenoura, P. Comon, A. Karfoul, F. Wendling, L. Senhadji y I. Merlet. "ICA-Based EEG denoising: a comparative analysis of fifteen methods". Bulletin of the Polish Academy of Sciences: Technical Sciences 60, n.º 3 (1 de diciembre de 2012): 407–18. http://dx.doi.org/10.2478/v10175-012-0052-3.
Texto completoŠtastný, Jakub y Pavel Sovka. "High-Resolution Movement EEG Classification". Computational Intelligence and Neuroscience 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/54925.
Texto completoLiang, Shuang y Lu Li. "Reconstruction of EEG Signal Based on Compressed Sensing and Wavelet Transform". Applied Mechanics and Materials 734 (febrero de 2015): 617–20. http://dx.doi.org/10.4028/www.scientific.net/amm.734.617.
Texto completoLu, Junru y Na Ni. "Application of Wavelet Transform in The Construction of Short-term Memory EEG Information Transmission Model". International Journal of Education and Humanities 7, n.º 3 (23 de marzo de 2023): 149–52. http://dx.doi.org/10.54097/ijeh.v7i3.6356.
Texto completoLi, Min, Wuhong Wang, Zhen Liu, Mingjun Qiu y Dayi Qu. "Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory". Sustainability 14, n.º 11 (6 de junio de 2022): 6901. http://dx.doi.org/10.3390/su14116901.
Texto completoPratiwi, Nor Kumalasari Caecar, Rita Magdalena, Yunendah Nur Fuadah, Sofia Saidah, Syamsul Rizal y Muhamad Rokhmat Isnaini. "Denoising Sinyal EEG dengan Algoritma Recursive Least Square dan Least Mean Square". TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol 5, n.º 2 (27 de noviembre de 2019): 122–29. http://dx.doi.org/10.15575/telka.v5n2.122-129.
Texto completoKumar, B. Krishna. "Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals". Biomedical and Pharmacology Journal 14, n.º 1 (30 de marzo de 2021): 425–33. http://dx.doi.org/10.13005/bpj/2142.
Texto completoZhang, Haoming, Mingqi Zhao, Chen Wei, Dante Mantini, Zherui Li y Quanying Liu. "EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising". Journal of Neural Engineering 18, n.º 5 (1 de octubre de 2021): 056057. http://dx.doi.org/10.1088/1741-2552/ac2bf8.
Texto completoHofmanis, Janis, Olivier Caspary, Valerie Louis-Dorr, Radu Ranta y Louis Maillard. "Denoising Depth EEG Signals During DBS Using Filtering and Subspace Decomposition". IEEE Transactions on Biomedical Engineering 60, n.º 10 (octubre de 2013): 2686–95. http://dx.doi.org/10.1109/tbme.2013.2262212.
Texto completoMartinez-Murcia, Francisco J., Andres Ortiz, Juan Manuel Gorriz, Javier Ramirez, Pedro Javier Lopez-Abarejo, Miguel Lopez-Zamora y Juan Luis Luque. "EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia". International Journal of Neural Systems 30, n.º 07 (28 de mayo de 2020): 2050037. http://dx.doi.org/10.1142/s0129065720500379.
Texto completoSardouie, Sepideh Hajipour, Laurent Albera, Mohammad Bagher Shamsollahi y Isabelle Merlet. "An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising". IEEE Signal Processing Letters 22, n.º 8 (agosto de 2015): 1198–202. http://dx.doi.org/10.1109/lsp.2014.2385868.
Texto completoXu, Peng y Dezhong Yao. "A novel method based on realistic head model for EEG denoising". Computer Methods and Programs in Biomedicine 83, n.º 2 (agosto de 2006): 104–10. http://dx.doi.org/10.1016/j.cmpb.2006.06.002.
Texto completoBalamareeswaran, M. y D. Ebenezer. "Denoising of EEG signals using Discrete Wavelet Transform based Scalar Quantization". Biomedical and Pharmacology Journal 8, n.º 1 (30 de junio de 2015): 399–406. http://dx.doi.org/10.13005/bpj/627.
Texto completoAl-Qazzaz, Noor Kamal, Alaa A. Aldoori y A. Buniya. "EEG Neuro-markers to Enhance BCI-based Stroke Patients Rehabilitation". International Journal on Engineering, Science and Technology 5, n.º 1 (15 de junio de 2023): 42–53. http://dx.doi.org/10.46328/ijonest.139.
Texto completoKumar, R. Suresh y P. Manimegalai. "Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals". Current Signal Transduction Therapy 15, n.º 1 (31 de julio de 2020): 77–83. http://dx.doi.org/10.2174/1574362414666190613142424.
Texto completoSaavedra, Carolina, Rodrigo Salas y Laurent Bougrain. "Wavelet-Based Semblance Methods to Enhance the Single-Trial Detection of Event-Related Potentials for a BCI Spelling System". Computational Intelligence and Neuroscience 2019 (26 de agosto de 2019): 1–10. http://dx.doi.org/10.1155/2019/8432953.
Texto completoDing, Bin, Fuxiao Tian y Li Zhao. "Digital Evaluation Algorithm for Upper Limb Motor Function Rehabilitation Based on Micro Sensor". Journal of Medical Imaging and Health Informatics 11, n.º 2 (1 de febrero de 2021): 391–401. http://dx.doi.org/10.1166/jmihi.2021.3278.
Texto completoSedik, Ahmed, Mohamed Marey y Hala Mostafa. "WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform". Applied Sciences 13, n.º 5 (21 de febrero de 2023): 2785. http://dx.doi.org/10.3390/app13052785.
Texto completoRanjan, Rakesh, Bikash Chandra Sahana y Ashish Kumar Bhandari. "Motion Artifacts Suppression From EEG Signals Using an Adaptive Signal Denoising Method". IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–10. http://dx.doi.org/10.1109/tim.2022.3142037.
Texto completoAn Peng. "Research on The EEG Signal Denoising Method Based on Improved Wavelet Transform". International Journal of Digital Content Technology and its Applications 7, n.º 4 (28 de febrero de 2013): 154–63. http://dx.doi.org/10.4156/jdcta.vol7.issue4.20.
Texto completoZhang, Shuoyue, Jürgen Hennig y Pierre LeVan. "Direct modelling of gradient artifacts for EEG-fMRI denoising and motion tracking". Journal of Neural Engineering 16, n.º 5 (6 de agosto de 2019): 056010. http://dx.doi.org/10.1088/1741-2552/ab2b21.
Texto completoSaleh, Majd, Ahmad Karfoul, Amar Kachenoura, Isabelle Merlet y Laurent Albera. "Efficient Stepsize Selection Strategy for Givens Parametrized ICA Applied to EEG Denoising". IEEE Signal Processing Letters 24, n.º 6 (junio de 2017): 882–86. http://dx.doi.org/10.1109/lsp.2017.2696359.
Texto completoAlyasseri, Zaid Abdi Alkareem, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Ammar Kamal Abasi y Sharif Naser Makhadmeh. "EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods". IEEE Access 8 (2020): 10584–605. http://dx.doi.org/10.1109/access.2019.2962658.
Texto completoNavarro, X., F. Porée, A. Beuchée y G. Carrault. "Denoising preterm EEG by signal decomposition and adaptive filtering: A comparative study". Medical Engineering & Physics 37, n.º 3 (marzo de 2015): 315–20. http://dx.doi.org/10.1016/j.medengphy.2015.01.006.
Texto completoRakibul Mowla, Md, Siew-Cheok Ng, Muhammad S. A. Zilany y Raveendran Paramesran. "Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising". Biomedical Signal Processing and Control 22 (septiembre de 2015): 111–18. http://dx.doi.org/10.1016/j.bspc.2015.06.009.
Texto completoUpadhyay, R., P. K. Padhy y P. K. Kankar. "EEG artifact removal and noise suppression by Discrete Orthonormal S-Transform denoising". Computers & Electrical Engineering 53 (julio de 2016): 125–42. http://dx.doi.org/10.1016/j.compeleceng.2016.05.015.
Texto completoAl-Qazzaz, Noor Kamal, Alaa A. Aldoori, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Ahmed Kazem Mohammed y Mustafa Ibrahim Mohyee. "EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients’ Rehabilitation". Sensors 23, n.º 8 (11 de abril de 2023): 3889. http://dx.doi.org/10.3390/s23083889.
Texto completoSweeney-Reed, Catherine M., Slawomir J. Nasuto, Marcus F. Vieira y Adriano O. Andrade. "Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review". Advances in Data Science and Adaptive Analysis 10, n.º 02 (abril de 2018): 1840001. http://dx.doi.org/10.1142/s2424922x18400016.
Texto completoJukic, Samed, Muzafer Saracevic, Abdulhamit Subasi y Jasmin Kevric. "Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals". Mathematics 8, n.º 9 (2 de septiembre de 2020): 1481. http://dx.doi.org/10.3390/math8091481.
Texto completoChaddad, Ahmad, Yihang Wu, Reem Kateb y Ahmed Bouridane. "Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques". Sensors 23, n.º 14 (16 de julio de 2023): 6434. http://dx.doi.org/10.3390/s23146434.
Texto completoDalal, Virupaxi y Satish Bhairannawar. "Efficient de-noising technique for electroencephalogram signal processing". IAES International Journal of Artificial Intelligence (IJ-AI) 11, n.º 2 (1 de junio de 2022): 603. http://dx.doi.org/10.11591/ijai.v11.i2.pp603-612.
Texto completoLi, Zhiwei, Jun Li, Yousheng Xia, Pingfa Feng y Feng Feng. "Variation Trends of Fractal Dimension in Epileptic EEG Signals". Algorithms 14, n.º 11 (29 de octubre de 2021): 316. http://dx.doi.org/10.3390/a14110316.
Texto completoYang, Biao, Jinmeng Cao, Tiantong Zhou, Li Dong, Ling Zou y Jianbo Xiang. "Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis". Computational and Mathematical Methods in Medicine 2018 (2 de julio de 2018): 1–11. http://dx.doi.org/10.1155/2018/3018356.
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