Results 61 to 70 of about 186,893 (322)

Common Practices in Clinical Electroencephalography

Korean Journal of Clinical Laboratory Science, 2021
Electroencephalography (EEG) provides the most accurate and quickest diagnosis of epilepsy. It is also an important examination for the real-time evaluation of brain function and seizures, no matter where.
Soon-Chul Hyun, Dongyeop Kim
doaj   +1 more source

Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics

Advanced Robotics Research, EarlyView.
This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang, Zhipeng Jiang, Sun Hwa Kwon, Matthew Ibrahim, Alexander Dang, Lin Dong   +5 more
wiley   +1 more source

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better? [PDF]

, 2013
Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique.
al-Ketbi, Omar, Conrad, Marc
core   +1 more source

Bioelectronic Sensors for Neuromuscular Perception in Human‐Machine Interfaces

Advanced Robotics Research, EarlyView.
This review summarizes recent advances in bioelectronic sensors for neuromuscular perception in human‐machine interfaces. By integrating biopotential, electrical impedance, and electrochemical sensing strategies with flexible electrode interfaces, these bioelectronic sensing systems enable intuitive, real‐time detection of muscle and nerve activity ...
Junwei Li, Bangyan Niu, Kunlin Wu, Jingcheng Xiao, Xiaoyan Liu, Yifan Wang   +5 more
wiley   +1 more source

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multi-wavelets [PDF]

, 2008
A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation.
Billings, S.A., Liu, J., Wei, H.L.
core  

TVB C++: A Fast and Flexible Back‐End for The Virtual Brain

Advanced Science, EarlyView.
TVB C++ is a streamlined and fast C++ Back‐End for The Virtual Brain (TVB), designed to make it as flexible as TVB, and FAST. Another pillar is to be fully compatible with TVB so easy bindings can be created from Python. Users can easily configure TVB C++ to execute the same code but with enhanced performance and parallelism.
Ignacio Martín, Gorka Zamora‐López, Jan Fousek, Michael Schirner, Petra Ritter, Viktor Jirsa, Gustavo Deco, Gustavo Patow   +7 more
wiley   +1 more source

Online home appliance control using EEG-Based brain-computer interfaces [PDF]

, 2019
Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at ...
Cho, Jeongho, Hwang, Minho, Kim, Hyun-Young, Kim, Min-Ki, Kim, Minju, Kim, Sung-Phil   +5 more
core   +1 more source

Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition

Advanced Science, EarlyView.
The study proposes a causal network markers (CNMs) framework to identify early‐warning signals preceding critical transitions. It validates CNMs on various computational benchmark models and real‐world datasets, demonstrating higher accuracy and flexibility compared to existing approaches.
Shirui Bian, Zezhou Wang, Siyang Leng, Wei Lin, Jifan Shi   +4 more
wiley   +1 more source

A real-time wireless wearable electroencephalography system based on Support Vector Machine for encephalopathy daily monitoring

International Journal of Distributed Sensor Networks, 2018
Wearable electroencephalography systems of out-of-hospital can both provide complementary recordings and offer several benefits over long-term monitoring. However, several limitations were present in these new-born systems, for example, uncomfortable for
Qing Zhang, Pingping Wang, Yan Liu, Bo Peng, Yufu Zhou, Zhiyong Zhou, Baotong Tong, Bensheng Qiu, Yishan Zheng, Yakang Dai   +9 more
doaj   +1 more source

Inverse Modeling for MEG/EEG data

, 2017
We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently ...
A Doucet, A Galka, A Gramfort, A Pascarella, A Sorrentino, A Sorrentino, A Sorrentino, C Campi, C Lamus, CJ Long, D Calvetti, DK Nguyen, E Somersalo, F Cardinale, F Pasquale de, F-H Lin, G Dassios, GR Barnes, H Murakami, JC Mosher, JC Mosher, JM Antelis, JR Wolpaw, K Sekihara, K Uutela, K Uutela, L Miao, L Rabiner, M Hämäläinen, M Kowalski, MS Hämäläinen, PJ Green, R Bouet, RG Andrzejak, S Pursiainen, S Sommariva, SC Jun, TS Tian, V Vivaldi, W Ou, X Chen   +40 more
core   +1 more source