Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Alexander Craik"'
Autor:
Alexander Craik, Juan José González-España, Ayman Alamir, David Edquilang, Sarah Wong, Lianne Sánchez Rodríguez, Jeff Feng, Gerard E. Francisco, Jose L. Contreras-Vidal
Publikováno v:
Sensors, Vol 23, Iss 13, p 5930 (2023)
Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approa
Externí odkaz:
https://doaj.org/article/e4e2b2611d45475c9d7b5e961164d926
Autor:
Contreras-Vidal, Alexander Craik, Juan José González-España, Ayman Alamir, David Edquilang, Sarah Wong, Lianne Sánchez Rodríguez, Jeff Feng, Gerard E. Francisco, Jose L.
Publikováno v:
Sensors; Volume 23; Issue 13; Pages: 5930
Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::f8b9ed6b5bc2015aabe5d71301f32962
Autor:
Sho Nakagome, Alexander Craik, Akshay Sujatha Ravindran, Yongtian He, Jesus G. Cruz-Garza, Jose L. Contreras-Vidal
Publikováno v:
Handbook of Neuroengineering ISBN: 9789811528484
Handbook of Neuroengineering ISBN: 9789811655395
Handbook of Neuroengineering ISBN: 9789811655395
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdad0f54d1778e40883f6ce4ae8c5a1c
https://doi.org/10.1007/978-981-15-2848-4_78-1
https://doi.org/10.1007/978-981-15-2848-4_78-1
Autor:
Hamid Fekri Azgomi, Alexander Craik, Sankalp Parekh, Joseph T. Francis, Alexander G. Steele, Jose L. Contreras-Vidal, Rose T. Faghih, Sandipan Pati, Mohammad Badri Ahmadi
Publikováno v:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Real-time continuous tracking of seizure state is necessary to develop feedback neuromodulation therapy that can prevent or terminate a seizure early. Due to its high temporal resolution, high scalp coverage, and non-invasive applicability, electroen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f59d61e56d013105be5bd11813bc8d0
https://pubmed.ncbi.nlm.nih.gov/34543199
https://pubmed.ncbi.nlm.nih.gov/34543199
Autor:
Niell Gorman, Jose L. Contreras-Vidal, Antoinette Louw, Alexander Craik, Jeff Feng, Jose Gonzalez
Publikováno v:
Lecture Notes in Networks and Systems ISBN: 9783030800901
AHFE (17)
AHFE (17)
Mobile brain-body imaging (MoBI) technology allows the study of the brain in action and the context of complex natural settings. MoBI devices are wearable devices that typically record the scalp electroencephalogram (EEG) and head motion of the user.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a2078f6bf92b2947816c5e333c4a9d0b
https://doi.org/10.1007/978-3-030-80091-8_1
https://doi.org/10.1007/978-3-030-80091-8_1
Autor:
Hamid Fekri Azgomi, Mohammad Badri Ahmadi, Jose L. Contreras-Vidal, Joseph T. Francis, Rose T. Faghih, Alexander Craik
Publikováno v:
ACSSC
Accurate and cost-effective seizure severity tracking is an important step towards limiting the negative effects of seizures in epileptic patients. Electroencephalography (EEG) is employed as a means to track seizures due to its high temporal resolut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a011b4df5581d3903b4b13c9ddca468
https://doi.org/10.1109/ieeeconf44664.2019.9048990
https://doi.org/10.1109/ieeeconf44664.2019.9048990
Publikováno v:
SMC
In this communication, a translational roadmap for a noninvasive Brain Machine Interface (BMI) system for rehabilitation is presented. This multi-faceted project addresses important engineering, clinical, end user and regulatory challenges. The goal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07d44ad52192f9274e334ded9e006d13
https://doi.org/10.1109/smc.2019.8914210
https://doi.org/10.1109/smc.2019.8914210
Publikováno v:
EMBC
The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd55034bb8e65a758d81dc1d4d2f04e2
https://doi.org/10.1109/embc.2019.8857575
https://doi.org/10.1109/embc.2019.8857575
Publikováno v:
Journal of Neural Engineering. 16:031001
Objective Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc8fd0d3a53939f63a476e12ff4d4c9a
https://doi.org/10.1088/1741-2552/ab0ab5
https://doi.org/10.1088/1741-2552/ab0ab5
Publikováno v:
Journal of Neural Engineering; Jun2019, Vol. 16 Issue 3, p1-1, 1p