Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces
By Carmen Vidaurre, Motoaki Kawanabe, Paul von Bünau, Benjamin Blankertz, K. Müller. IEEE Transactions on Biomedical Engineering, 2011
302 citations recorded by the source corpus.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/2a413bfa4c42a5ead22628385187889bb3333bae
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- Florian Yger
- Cuntai Guan
- O-Yeon Kwon
- Min-Ho Lee
- Seong-Whan Lee
- Fabien Lotte
- Andrzej S. Cichocki
- Marco Congedo
In Cognitive Neuroscience
- Fabien Lotte
- Cuntai Guan
- Florian Yger
- O-Yeon Kwon
- Min-Ho Lee
- Seong-Whan Lee
- Marco Congedo
- Andrzej S. Cichocki
In Human-Computer Interaction
Citations in the corpus
- Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, methodology): “…data is available to calibrate the BCI for a given user, another approach that can be used to still reach reasonable classification performance is to use an adaptive approach [46], [47], i.e., to refine the calibration of the BCI during use, as new EEG data (labeled or not) become available.”
- A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “Another simple unsupervised adaptation of the LDA classifier for motor imagery data was proposed and evaluated for both offline and online data [219].”
- Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, methodology): “This is because LDA not only achieved the highest accuracy in the subject-independent environment previously [32] but also is widely utilized in the subject-dependent environment [37].”