Comparison of Classifiers and Statistical Analysis for EEG Signals Used in Brain Computer Interface Motor Task Paradigm

Eva, Oana Diana; Lazar, Anca Mihaela

doi:10.14569/ijarai.2015.040102

Cited by 7 publications

(1 citation statement)

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“…LDA helps locate projection to maximize the separation between samples for class separation 88 . The projection is done by linearly transforming data from a high dimensional space to a low dimensional space, in which finally the decision is made in the low dimensional space 89 . The purpose of the LDA is to change the original dataset to a lower-dimensional space with good sample discrimination.…”

Section: Methodsmentioning

confidence: 99%

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

et al. 2023

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Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

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Section: Methodsmentioning

confidence: 99%