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| BESSERVE,MICHEL; JERBI,KARIM; LAURENT,FRANCOIS; BAILLET,SYLVAIN; MARTINERIE,JACQUES; GARNERO,LINE. |
| Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the... |
| Tipo: Journal article |
Palavras-chave: Brain computer interface; Electroencephalography; Magnetoencephalography; Visuomotor control; Support Vector Machine. |
| Ano: 2007 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500005 |
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| Debaene,Guillaume; Pikuła,Dorota; Niedźwiecki,Jacek. |
| The objective of this research was to investigate the effects of a long-term experiment on soil spectral properties and to develop prediction models of these properties (soil organic carbon (SOC), N, pH, Hh, P2O5, K2O, Ca, Mg, K, and Na content) from texturally homogeneous samples (loamy sand). To this aim, chemometric techniques, such as partial least square (PLS) regression and support vector machine (SVM) classification, were used. Our results show that visible and near infrared spectroscopy (VIS-NIRS) is suitable for the prediction of properties of texturally homogeneous samples. The effects of fertilizer applications were sufficient to modify the soil chemical composition to a range suitable for using VIS-NIRS for calibration and modeling purposes.... |
| Tipo: Journal article |
Palavras-chave: Manure; Near-infrared spectroscopy; Nitrogen fertilizer; Partial least square regression; Soil organic carbon; Support Vector Machine. |
| Ano: 2014 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202014000100003 |
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