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» Support feature machine for classification of abnormal brain...
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ICMLA
2010
13 years 3 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...
ICMLA
2009
13 years 3 months ago
Feature Extraction and Classification of EEG Signals for Rapid P300 Mind Spelling
The Mind Speller is a Brain-Computer Interface which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electroencephalograms....
Adrien Combaz, Nikolay V. Manyakov, Nikolay Chumer...
ISCAS
2006
IEEE
116views Hardware» more  ISCAS 2006»
13 years 11 months ago
Signal processing for brain-computer interface: enhance feature extraction and classification
Abstract-In this paper we present a new scheme for brain imaginary movement invovles sophisticated spatial-temporalsignal processing and classification for electroencephalogram spe...
Haihong Zhang, Cuntai Guan, Yuanqing Li
BMCBI
2011
12 years 9 months ago
Conotoxin Protein Classification Using Free Scores of Words and Support Vector Machines
Background: Conotoxin has been proven to be effective in drug design and could be used to treat various disorders such as schizophrenia, neuromuscular disorders and chronic pain. ...
Nazar Zaki, Stefan Wolfsheimer, Grégory Nue...
MICCAI
2005
Springer
14 years 6 months ago
Exploiting Temporal Information in Functional Magnetic Resonance Imaging Brain Data
Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data...
Lei Zhang 0002, Dimitris Samaras, Dardo Tomasi, Ne...