A New Signal Processing Approach for Discrimination of EEG Recordings

Hossein Hassani, Mohammad Reza Yeganegi and Emmanuel Sirimal Silva
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Hossein Hassani: Research Institute for Energy Management and Planning of University of Tehran, No. 9, Ghods St., Enghelab St., Tehran 1417466191, Iran
Mohammad Reza Yeganegi: Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran
Emmanuel Sirimal Silva: Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK

Stats, 2018, vol. 1, issue 1, 1-14

Abstract: Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are compared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.

Keywords: EEG; singular spectrum analysis; time series discriminant analysis; kernel; principal component analysis (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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