Abstract
In this brief contribution I will discuss recent directions of our research where nonlinear learning methods are employed for analysing multimodal brain data, both in the context of BCI and beyond-essentially summarizing some steps taken by the BBCI team and co-workers. Clearly, unavoidably and intentionally this abstract will have a high overlap to prior own contributions and touch upon ongoing unpublished respectively pre-pulished work, nevertheless extensively providing pointers to various research directions.
Original language | English |
---|---|
Title of host publication | 8th International Winter Conference on Brain-Computer Interface, BCI 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728147079 |
DOIs | |
Publication status | Published - 2020 Feb |
Event | 8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of Duration: 2020 Feb 26 → 2020 Feb 28 |
Publication series
Name | 8th International Winter Conference on Brain-Computer Interface, BCI 2020 |
---|
Conference
Conference | 8th International Winter Conference on Brain-Computer Interface, BCI 2020 |
---|---|
Country/Territory | Korea, Republic of |
City | Gangwon |
Period | 20/2/26 → 20/2/28 |
Bibliographical note
Funding Information:This abstract is based on joint work with Till Nierhaus, Carmen Vidaurre, Arno Villringer, Armin Thomas, Wojciech Samek, Hauke Heekeren, Benjamin Blankertz, Gabriel Curio, Michael Tangermann, Siamac Fazli, Vadim Nikulin, Gregoire Montavon, Sebastian Bach/Lapuschkin, Irene Sturm and many other members of the Berlin Brain Computer Interface team, the machine learning groups and many more esteemed collaborators. We greatly acknowledge funding by BMBF, EU, DFG and NRF.
Publisher Copyright:
© 2020 IEEE.
ASJC Scopus subject areas
- Behavioral Neuroscience
- Cognitive Neuroscience
- Artificial Intelligence
- Human-Computer Interaction