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.
Highlights
Discriminating electroencephalogram, EEG, signals are of great interest in both biology and statistics [1,2,3,4]
This paper begins with the aim of introducing a new and more effective approach for discriminating EEG signals
We propose the use of the nonparametric singular spectrum analysis technique as a viable tool and evaluate its performance at discriminating EEG signals using the [1] benchmark data set for epileptic study
Summary
Research Institute for Energy Management and Planning of University of Tehran, No 9, Ghods St., Enghelab St., Tehran 1417466191, Iran Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran; Received: 19 September 2018; Accepted: 3 November 2018; Published: 8 November 2018
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