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» SVM feature selection for multidimensional EEG data
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ICASSP
2011
IEEE
12 years 8 months ago
SVM feature selection for multidimensional EEG data
In many machine learning applications, like Brain - Computer Interfaces (BCI), only high-dimensional noisy data are available rendering the discrimination task non-trivial. In thi...
Nisrine Jrad, Ronald Phlypo, Marco Congedo
ICPR
2004
IEEE
14 years 5 months ago
High Accuracy Classification of EEG Signal
Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accu...
Chng Eng Siong, Cuntai Guan, Jiankang Wu, M. Thula...
ICMLA
2010
13 years 2 months ago
Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets
Abstract--Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as s...
Drausin Wulsin, Justin Blanco, Ram Mani, Brian Lit...
ICRA
2008
IEEE
185views Robotics» more  ICRA 2008»
13 years 11 months ago
Human detection using multimodal and multidimensional features
— This paper presents a novel human detection method based on a Bayesian fusion approach using laser range data and camera images. Laser range data analysis groups data points wi...
Luciano Spinello, Roland Siegwart
KDD
2007
ACM
202views Data Mining» more  KDD 2007»
14 years 5 months ago
Support feature machine for classification of abnormal brain activity
In this study, a novel multidimensional time series classification technique, namely support feature machine (SFM), is proposed. SFM is inspired by the optimization model of suppo...
Wanpracha Art Chaovalitwongse, Ya-Ju Fan, Rajesh C...