Toward Brain-Computer Interfacing
By K. Jellinger. 2009
505 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/7b9d85bbdac09ec4481ce2bb46a843f4d60b1e54
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- K. Müller
- Benjamin Blankertz
- Matthias Krauledat
- Fabien Lotte
- Cuntai Guan
- Gabriel Curio
- Guido Dornhege
- Steven Lemm
Citations in the corpus
- Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces (2009, unknown): “CC BCI systems can also be based on other components of brain signals, see Dornhege et al. (2007); Allison et al. (2007); Birbaumer et al. (2006); Pfurtscheller et al. (2005); Wolpaw et al. (2002); K¨ubler et al. (2001) for an overview, but only SMR-based BCIs are considered in this paper.”
- Single-trial analysis and classification of ERP components - A tutorial (2011, unknown): “This branch of research is strongly influenced by the development of an effective communication interface connecting the human brain and a computer (Dornhege et al., 2007; Kübler and Kotchoubey, 2007; Kübler and Müller, 2007; Wolpaw, 2007; Birbaumer, 2006; Pfurtscheller et al., 2005; Curran and Stok”
- Brain computer interfacing: Applications and challenges (2015, background): “Thus the usage of invasive recording in real world has been usually restricted to the BCI based medical applications for a few disabled users [94,5].”
- Introduction to machine learning for brain imaging (2011, methodology): “Clearly, in this development the fi eld of real-time decoding for brain – computer interfacing (BCI) has been a technology motor (e.g. Dornhege et al., 2007; Kübler and Kotchoubey, 2007; Kübler and Müller, 2007; Wolpaw, 2007; Birbaumer, 2006; Pfurtscheller et al.,…”
- Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, background): “For instance, the work of [180] presents one of the first uses of DNNs explanation techniques in cognitive neurosciences, specifically in brain–computer interfacing [40] where linear methods are still the most widely used filtering methods [24], [63].”
- Brain-Computer Interfaces (2015, unknown): “There is growing interest for BCI in the machine learning and signal processing communities, as evidenced by published proceedings [2] and data analysis competitions [3]–[5].”
- The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects (2008, background): “, for a computer application or a neuroprosthesis [3]–[8].”