Results 81 to 90 of about 3,887,920 (279)

Multichannel EEG Signal Classification -A Geometric Approach [PDF]

, 2010
The study of the different sleep stages of a patient using his/her recorded EEG signals falls in the area of signal classification. In general, this involves extracting from the EEG signals, a signal feature on which the classification is performed.
Li, Yili
core  

Clustering technique-based least square support vector machine for EEG signal classification

, 2011
This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been
Siuly, S., Li, Yan, Wen, Peng (Paul)
core   +1 more source

Degradable, Skin‐Conformal Nanocomposite‐Based Bioelectronic Patches for High‐Fidelity Electrophysiology and Gesture‐Controlled Robotics

Advanced Functional Materials, EarlyView.
A degradable, skin‐conformal bioelectronic patch combines a starch‐dominated nanocomposite conductor with a gelatin substrate to enable sensitive strain sensing, high‐fidelity electrocardiography, electromyography, electrooculography, and machine‐learning‐assisted gesture control.
Ming Dong, Hongna Yuan, Shubo Liu, Abdulkadir Sanli, Emiliano Bilotti, Han Zhang, Pietro Cataldi, Karin Hing, Oliver Fenwick, Colin J. Humphreys, Dimitrios G. Papageorgiou   +10 more
wiley   +1 more source

Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs. [PDF]

, 2006
Two novel approaches to the problem of brain signals (electroencephalogram (EEG)) classification are introduced in the paper. The first method is based on a modular probabilistic network architecture that employs multiple dependant hidden Markov models ...
Angelov, Plamen, Chiao, Shih-Yang, Reoullas, Michalis, Xydeas, Costas   +3 more
core   +4 more sources

The Best Artificial Neural Network Parameters for Electroencephalogram Classification Based on Discrete Wavelet Transform

Nano Biomedicine and Engineering, 2019
This paper presents the classification of electroencephalogram (EEG) signals using artificial neural network techniques. The signal processing of EEG signal could provide several areas for research in biomedical field.
Mousa Kadhim Wali
doaj   +1 more source

Blood–Brain Barrier Regulation: Evolving From Classic Strategies to Electrochemical Ion and Reactive Oxygen Species Control

Advanced Healthcare Materials, EarlyView.
Conventional therapies suffer from poor blood–brain barrier (BBB) penetration and disordered ion/reactive oxygen species (ROS) homeostasis, hindering precise neurological treatment. Electrochemical strategies achieve accurate spatiotemporal regulation of neural homeostasis.
Xiaokang Hu, Caiyu Liu, Meijun Pang, Zhiyuan Shi, Dong Ming, Xiuyun Liu   +5 more
wiley   +1 more source

A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System

IEEE Access, 2017
With the development of the wearable brain-computer interface (BCI), a few-channel BCI system is necessary for its application to daily life. In this paper, we proposed a bimodal BCI system that uses only a few channels of electroencephalograph (EEG) and
Sheng Ge, Qing Yang, Ruimin Wang, Pan Lin, Junfeng Gao, Yue Leng, Yuankui Yang, Haixian Wang   +7 more
doaj   +1 more source

Analysis and classification of EEG signals [PDF]

, 2012
Electroencephalography (EEG) is one of the most clinically and scientifically exploited signals recorded from humans. Hence, its measurement plays a prominent role in brain studies. In particular, the examination of EEG signals has been recognized as the

core  

Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI [PDF]

, 2007
The general linear model (GLM) has been used to analyze simultaneous EEG–fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG.
Lemieux, L., de Martino, F.; id_orcid, De Martino, F., Formisano, E., Duncan, J.S., Laufs, H., Rodionov, R., Carmichael, D.W., Walker, M., Formisano, E.; id_orcid   +9 more
core   +2 more sources

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source