7 August 2017 Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis (Erratum)
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Proceedings Volume 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017; 104453X (2017) https://doi.org/10.1117/12.2281066
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2017, 2017, Wilga, Poland
Abstract
Publisher’s Note: This paper, originally published on 8 August 2017, was replaced with a corrected/revised version on12 September 2017. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: normal, ictal and postictal. The classification error above 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation pathology states.
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B. Szuflitowska, B. Szuflitowska, P. Orlowski, P. Orlowski, } "Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis (Erratum)", Proc. SPIE 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 104453X (7 August 2017); doi: 10.1117/12.2281066; https://doi.org/10.1117/12.2281066
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