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ICASSP
2007
IEEE
14 years 4 months ago
A Self-Training Semi-Supervised Support Vector Machine Algorithm and its Applications in Brain Computer Interface
In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation in small trainin...
Yuanqing Li, Huiqi Li, Cuntai Guan, Zhengyang Chin
NIPS
2004
13 years 11 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Pradeep Shenoy, Rajesh P. N. Rao
IWANN
2009
Springer
14 years 4 months ago
Asynchronous Brain-Computer Interface to Navigate in Virtual Environments Using One Motor Imagery
A Brain-Computer Interface (BCI) application focused on the control of a wheelchair must consider the danger which a wrong command would involve in a real situation. Virtual realit...
Francisco Velasco-Álvarez, Ricardo Ron-Ange...
IJCNN
2008
IEEE
14 years 4 months ago
Multifractal feature vectors for Brain-Computer interfaces
—This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI comp...
Nicolas Brodu
ICASSP
2010
IEEE
13 years 7 months ago
Learning from other subjects helps reducing Brain-Computer Interface calibration time
A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. I...
Fabien Lotte, Cuntai Guan