1. EEG channel and feature investigation in binary and multiple motor imagery task predictionsMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2024, izvirni znanstveni članek Ključne besede: brain-computer interfaces, electroencephalogram, feature and channel investigation, feature selection, machine learning, motor imagery task classification
Objavljeno v DKUM: 19.12.2024; Ogledov: 0; Prenosov: 4
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2. Statistically significant features improve binary and multiple motor imagery task predictions from EEGsMurside Degirmenci, Yilmaz Kemal Yuce, Matjaž Perc, Yalcin Isler, 2023, izvirni znanstveni članek Opis: In recent studies, in the field of Brain-Computer Interface (BCI), researchers have
focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram
(EEG) signals provide the interaction and communication between the paralyzed
patients and the outside world for moving and controlling external devices
such as wheelchair and moving cursors. However, current approaches in the
Motor Imagery-BCI system design require.
Ključne besede: brain-computer interfaces, electroencephalogram, feature selection, machine learning, task classification
Objavljeno v DKUM: 10.09.2024; Ogledov: 31; Prenosov: 8
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6. Classification of white varietal wines using chemical analysis and sensorial evaluationsKatja Šnuderl, Jan Mocak, Darinka Brodnjak-Vončina, Bibiana Sedláčkova, 2009, izvirni znanstveni članek Opis: The ways of application of multivariate data analysis and ANOVA to classification of white varietal wines are here demonstrated. Wine classification was performed using the following classification criteria: winevariety, year of production, wine producer, and wine quality, as found by sensorial testing (bouquet, colour, and taste). Subjective wine evaluation, made by wine experts, is combined with commonly used chemical and physico-chemical properties, measured in analytical laboratory. Importance of the measured variables was determined by principal component analysis and confirmed by analysis of variance. Linear discriminant analysis enabled not only a very successful wine classification but also prediction of the wine category for unknown samples. The wine categories were set up either by three wine varieties, or two vintages, wine producers; two or three wine categories established by wine quality reflected either total points obtained in sensorial evaluation or the points obtained for a particular quality descriptor like colour, taste and bouquet.
Ključne besede: multivariate data analysis, principal component analysis, discriminant analysis, feature selection, ANOVA, sensory analysis
Objavljeno v DKUM: 31.05.2012; Ogledov: 2308; Prenosov: 112
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