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Relevant bibliographies by topics / Bandpower

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Academic literature on the topic 'Bandpower'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Bandpower"

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Caviness, John N., Thomas G. Beach, Joseph G. Hentz, Holly A. Shill, Erika D. Driver-Dunckley, and Charles H. Adler. "Association Between Pathology and Electroencephalographic Activity in Parkinson’s Disease." Clinical EEG and Neuroscience 49, no. 5 (2017): 321–27. http://dx.doi.org/10.1177/1550059417696179.

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Introduction. The key mechanisms that connect Parkinson’s disease pathology with dementia are unclear. We tested the hypothesis that the quantitative spectral electroencephalographic measure, delta bandpower, correlates with Lewy type synucleinopathy on pathological examination in Parkinson’s disease. As a corollary hypothesis, we analyzed whether there would be delta bandpower electroencephalographic differences between Parkinson’s disease dementia cases with and without pathological criteria for Alzheimer’s disease. Methods. We used pathological examination results from 44 Parkinson’s diseas

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Jeakle, Eleanor N., Justin R. Abbott, Joshua O. Usoro, et al. "Chronic Stability of Local Field Potentials Using Amorphous Silicon Carbide Microelectrode Arrays Implanted in the Rat Motor Cortex." Micromachines 14, no. 3 (2023): 680. http://dx.doi.org/10.3390/mi14030680.

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Implantable microelectrode arrays (MEAs) enable the recording of electrical activity of cortical neurons, allowing the development of brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic conditions, prompting the development of novel MEAs that can improve long-term performance. Conventional planar, silicon-based devices and ultra-thin amorphous silicon carbide (a-SiC) MEAs were implanted in the motor cortex of female Sprague–Dawley rats, and weekly anesthetized recordings were made for 16 weeks after implantation. The spectral density and bandpower between

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Doppelmayr, Michael, W. Klimesch, P. Sauseng, K. Hödlmoser, W. Stadler, and S. Hanslmayr. "Intelligence related differences in EEG-bandpower." Neuroscience Letters 381, no. 3 (2005): 309–13. http://dx.doi.org/10.1016/j.neulet.2005.02.037.

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Nelson, Thomas, Brian Coughlin, Daniel Cahill, et al. "CNSC-22. CANCER NEUROSCIENCE IN THE OR: ELECTROPHYSIOLOGIC INSIGHTS AT THE TIME OF BRAIN TUMOR RESECTION." Neuro-Oncology 25, Supplement_5 (2023): v27. http://dx.doi.org/10.1093/neuonc/noad179.0106.

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Abstract Pioneering works by Venkatesh and Venkataramani demonstrated that formation of functional neural-glial synapses fosters development and growth of glial neoplasms. Resultant peritumoral neural hyperexcitability associates with tumor progression, though limited in vivo human data have been evaluated to date. We sought to enrich our understanding of these mechanisms by obtaining thin film intraoperative micro-electrocorticography (µECoG) recordings from patients undergoing primary brain tumor resection. METHODS: Intraoperative µECoG data for 8 patients with low- (LGG) and high-grade glio

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Dahne, Sven, Felix Biessmann, Frank C. Meinecke, Jan Mehnert, Siamac Fazli, and Klaus-Robert Muller. "Integration of Multivariate Data Streams With Bandpower Signals." IEEE Transactions on Multimedia 15, no. 5 (2013): 1001–13. http://dx.doi.org/10.1109/tmm.2013.2250267.

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Dalila, Trad, Al-Ani Tarik, and Jemni Mohamed. "BRAIN Journal - Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 2 (2016): 5–16. https://doi.org/10.5281/zenodo.1044300.

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ABSTRACT The idea that brain activity could be used as a communication channel has rapidly developed. Indeed, Electroencephalography (EEG) is the most common technique to measure the brain activity on the scalp and in real-time. In this study we examine the use of EEG signals in Brain Computer Interface (BCI). This approach consists of combining the Empirical Mode Decomposition (EMD) and band power (BP) for the extraction of EEG signals in order to classify motor imagery (MI). This new feature extraction approach is intended for non-stationary and non-linear characteristics MI EEG. The EMD met

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Hollenbenders, Y., and A. Reichenbach. "P-103 Robustness of electroencephalography biomarkers for major depressive disorder – An exemplary study with alpha bandpower." Clinical Neurophysiology 148 (April 2023): e55. http://dx.doi.org/10.1016/j.clinph.2023.02.120.

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Sugianela, Yuna, Qonita Luthfia Sutino, and Darlis Herumurti. "EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST." Jurnal Ilmu Komputer dan Informasi 11, no. 1 (2018): 27. http://dx.doi.org/10.21609/jiki.v11i1.549.

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EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomp

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Zych, Patryk, Kacper Filipek, Agata Mrozek-Czajkowska, and Piotr Kuwałek. "Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform." Sensors 25, no. 11 (2025): 3284. https://doi.org/10.3390/s25113284.

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Brain–computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need for physical interaction with external devices. This study investigates the performance of traditional classifiers—specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)—in comparison with a hybrid neural network model for EEG-based gesture classification. The datase

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Binias, Bartosz, Dariusz Myszor, and Krzysztof A. Cyran. "A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals." Computational Intelligence and Neuroscience 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2703513.

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This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipa

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Book chapters on the topic "Bandpower"

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Angulo-Sherman, I. N., M. Rodríguez-Ugarte, E. Iáñez, and J. M. Azorín. "Classification of Gait Motor Imagery While Standing Based on Electroencephalographic Bandpower." In Biomedical Applications Based on Natural and Artificial Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59773-7_7.

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Rusanu, Oana Andreea. "A LabVIEW Based Brain-Computer Interface Application for Controlling a Virtual Robotic Arm Using the P300 Evoked Biopotentials and the EEG Bandpower Rhythms Acquired from the GTEC Unicorn Headset." In IFMBE Proceedings. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42782-4_12.

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Korik, A., R. Sosnik, N. Siddique, and D. Coyle. "3D hand motion trajectory prediction from EEG mu and beta bandpower." In Progress in Brain Research. Elsevier, 2016. http://dx.doi.org/10.1016/bs.pbr.2016.05.001.

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Conference papers on the topic "Bandpower"

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Gruenwald, J., C. Kapeller, K. Kamada, J. Scharinger, and C. Guger. "Optimal bandpower estimation and tracking via Kalman filtering for real-time Brain-Computer Interfaces." In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2017. http://dx.doi.org/10.1109/ner.2017.8008424.

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Alba-Sanchez, F., O. Yanez-Suarez, and H. Brust-Carmona. "Assisted diagnosis of Attention-Deficit Hyperactivity Disorder through EEG bandpower clustering with self-organizing maps." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626360.

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Foong, Ruyi, Kai Keng Ang, and Chai Quek. "Correlation of reaction time and EEG log bandpower from dry frontal electrodes in a passive fatigue driving simulation experiment." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037360.

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