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Journal of Neural Engineering
IF
4.55
Papers
1954
Papers 2056
1 page of 206 pages (2,056 results)
Newest
#1Maro G. MachizawaH-Index: 7
#2Giuseppe LisiH-Index: 5
Last. Shigeto YamawakiH-Index: 50
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Source
#1Roberto Martin del Campo-Vera (SC: University of Southern California)
#2Angad S. Gogia (SC: University of Southern California)
Last. Charles Y. Liu (SC: University of Southern California)H-Index: 28
view all 12 authors...
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#1Xiaoyan Deng (SCUT: South China University of Technology)H-Index: 1
#2Zhu Liang Yu (SCUT: South China University of Technology)H-Index: 22
Last. Yuanqing Li (SCUT: South China University of Technology)H-Index: 4
view all 5 authors...
Source
#1Ashley N Dalrymple (Bionics Institute)
#2Mario Huynh (Bionics Institute)H-Index: 1
Last. Robert K. Shepherd (Bionics Institute)H-Index: 53
view all 9 authors...
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#2Ze WangH-Index: 3
Last. Yong HuH-Index: 27
view all 8 authors...
Source
#1Yu Huang (CUNY: City University of New York)H-Index: 11
#2Abhishek DattaH-Index: 37
Last. Lucas C. Parra (CUNY: City University of New York)H-Index: 51
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Source
#1Yin Tian (CQUPT: Chongqing University of Posts and Telecommunications)
#2Liang Ma (CQUPT: Chongqing University of Posts and Telecommunications)
Source
#1Z. J. Wang (Kwangwoon University)H-Index: 1
#2Eun-Seong Kim (Kwangwoon University)H-Index: 3
Last. Heung Dong Kim (Yonsei University)H-Index: 27
view all 8 authors...
OBJECTIVE: Vagus nerve stimulation (VNS) is a nonpharmacologic therapeutic option for patients who have pharmaco-resistant Dravet syndrome (DS). Plentiful efforts have been made for delivering VNS to DS patients, but its effectiveness still requires further verification. We investigated the effectiveness of the VNS treatment of DS patients using brain connectivity analysis with electroencephalography (EEG). APPROACH: Twenty pharmaco-resistant DS patients were selected to undergo VNS implantation...
Source
#1Yonghao Chen (THU: Tsinghua University)
#2Chen Yang (Beijing University of Posts and Telecommunications)
Last. Xiaorong Gao (THU: Tsinghua University)H-Index: 36
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OBJECTIVE: Filter bank canonical correlation analysis (FBCCA) is a widely-used classification approach implemented in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, conventional detection algorithms for SSVEP recognition problems, including the FBCCA, were usually based on 'fixed window' strategy. That's to say, these algorithms always analyze data with fixed length. This study devoted to enhance the performance of SSVEP-based BCIs by designing a ne...
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#1Tengjun Liu (SZU: Shenzhen University)
#2Gan Huang (SZU: Shenzhen University)H-Index: 14
Last. Zhiguo Zhang (SZU: Shenzhen University)
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OBJECTIVE: Brain Computer Interface (BCI) inefficiency indicates that there would be 10% to 50% of users are unable to operate Motor-Imagery-based BCI systems. Importantly, the almost all previous studieds on BCI inefficiency were based on tests of Sensory Motor Rhythm (SMR) feature. In this work, we assessed the occurrence of BCI inefficiency with SMR and Movement-Related Cortical Potential (MRCP) features. APPROACH: A pool of datasets of resting state and movements related EEG signals was reco...
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Top fields of study
Machine learning
Brain–computer interface
Computer vision
Computer science
Electroencephalography