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of 7
pro vyhledávání: '"Joshua Giles"'
Autor:
Salem Mansour, Joshua Giles, Kai Keng Ang, Krishnan P. S. Nair, Kok Soon Phua, Mahnaz Arvaneh
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-10 (2022)
Abstract Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required fo
Externí odkaz:
https://doaj.org/article/8dc72132cc6349c4bac3d1796f068962
Publikováno v:
Frontiers in Neuroergonomics, Vol 3 (2022)
Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significan
Externí odkaz:
https://doaj.org/article/3831718ec7444ac2a56bcef010410a62
Autor:
Earle, Joshua Giles
This dissertation examines how the human bodymind has been seen as malleable by science, technology, and policy practitioners from the Eugenic era in the United States in the first half of the 20th century, to the future imaginaries of Transhumanists
Externí odkaz:
http://hdl.handle.net/10919/107009
Publikováno v:
EMBC
A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4edb5e33c369d70eddbaea1a68560f11
https://doi.org/10.1109/embc44109.2020.9176635
https://doi.org/10.1109/embc44109.2020.9176635
Publikováno v:
EPiC Series in Health Sciences.
This paper describes the development, functionality, and initial testing of a wearable sensor system and companion smartphone app intended to support the rehabilitation of Achilles injury patients by providing 1) real time biofeedback, which can help
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e464c73cdb2e99d9e89fae0e61daa976
https://doi.org/10.29007/wh2k
https://doi.org/10.29007/wh2k
Publikováno v:
ICASSP
One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using limit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::826b9dd0d8689d8591c04eec67f1fdcc
https://doi.org/10.1109/icassp.2019.8683331
https://doi.org/10.1109/icassp.2019.8683331
Publikováno v:
EMBC
Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, most of these adaptation techniques are only suitable for binary-class BCIs. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41cf0169302ceb18a7993f864c530a7a
https://doi.org/10.1109/embc.2018.8512643
https://doi.org/10.1109/embc.2018.8512643