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JUCS
2006
185views more  JUCS 2006»
13 years 9 months ago
The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main co...
Benjamin Blankertz, Guido Dornhege, Steven Lemm, M...
TAMODIA
2008
13 years 11 months ago
Learning Key Contexts of Use in the Wild for Driving Plastic User Interfaces Engineering
This paper addresses software plasticity, i.e. the ability of interactive systems to adapt to context of use while preserving user-centered properties. In plasticity, a classical a...
Vincent Ganneau, Gaëlle Calvary, Rachel Demum...
IEEEVAST
2010
13 years 4 months ago
ALIDA: Using machine learning for intent discernment in visual analytics interfaces
In this paper, we introduce ALIDA, an Active Learning Intent Discerning Agent for visual analytics interfaces. As users interact with and explore data in a visual analytics enviro...
Tera Marie Green, Ross Maciejewski, Steve DiPaola
JMLR
2010
177views more  JMLR 2010»
13 years 4 months ago
Multitask Learning for Brain-Computer Interfaces
Brain-computer interfaces (BCIs) are limited in their applicability in everyday settings by the current necessity to record subjectspecific calibration data prior to actual use of...
Morteza Alamgir, Moritz Grosse-Wentrup, Yasemin Al...
C5
2005
IEEE
14 years 3 months ago
P2P e-Learning System and Its Squeak-Based User Interface
In this paper, we present a novel framework for asynchronous Web-based training. The proposed system has two distinguishing features. Firstly, it is based on P2P architecture for ...
Takao Kawamura, Ryosuke Nakatani, Kazunori Sugahar...