Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Joos Behncke"'
Autor:
Tonio Ball, Andreas Schulze-Bonhage, Petr Marusic, Robin Tibor Schirrmeister, Jiri Hammer, Martin Völker, Joos Behncke, Wolfram Burgard, Lukas D. J. Fiederer
Publikováno v:
SMC
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62b358cc3eba21b2ec7c8a6952da4434
https://doi.org/10.1109/smc.2018.00106
https://doi.org/10.1109/smc.2018.00106
Autor:
Petr Marusic, Wolfram Burgard, Andreas Schulze-Bonhage, Martin Völker, Jiri Hammer, Robin Tibor Schirrmeister, Tonio Ball, Joos Behncke
Publikováno v:
SMC
When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, diverse data sets, e.g., recorded from various subjects or tasks, can ev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8e670d26c00a8a08f0c58e10a79b7ea
https://doi.org/10.1109/smc.2018.00186
https://doi.org/10.1109/smc.2018.00186
Publikováno v:
Proceedings of the 6th International Congress on Neurotechnology, Electronics and Informatics.
For utilization of robotic assistive devices in everyday life, means for detection and processing of erroneous robot actions are a focal aspect in the development of collaborative systems, especially when controlled via brain signals. Though, the var
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8a60cfa80a9f8560066821668e07c8d
https://doi.org/10.5220/0006934900610066
https://doi.org/10.5220/0006934900610066
Autor:
Tonio Ball, Joos Behncke, Lukas D. J. Fiederer, Robin Tibor Schirrmeister, Felix A. Heilmeyer, Martin Völker
Publikováno v:
SMC
EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently emerging EEG decoding approach is deep learning with Convolutional or
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::348889f272cf4b8ffaed9ae619c65443
The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One important aspect here is the management of robot errors, including fast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c0feb767c010455b903f9c9c2f80105
Publikováno v:
Journal of Neuroscience Methods. 327:108396
Background Intracranial electroencephalography (iEEG) is increasingly used in neuroscientific research. However, the position of the implanted electrodes varies greatly between patients, which makes group analyses particularly difficult. Therefore, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4c8832014bb706e504bb24e53a29999
https://doi.org/10.1016/j.jneumeth.2019.108396
https://doi.org/10.1016/j.jneumeth.2019.108396