A Quasi-probabilistic distribution model for EEG Signal classification by using 2-D signal representation. (August 2018)

• Record Type: Journal Article
• Authors: Murat Yilmaz, Cagatay; Kose, Cemal; Hatipoglu, Bahar
• Is Part Of: Computer methods and programs in biomedicine. Volume 162(2018)
• Page Start: 187
• Page End: 196
• Publication Date: 2018-08
• Subjects: Electroencephalogram (EEG) -- Brain–computer interfaces (BCIs) -- Feature extraction -- Classification -- Motor imagery -- Time-domain features; 00-01 -- 99-00; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.05.026
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This paper proposes a new 2-D signal representation and quasi-probabilistic distribution method for binary classification of EEG records. New time domain features were introduced, and non-stationary EEG signals were represented in a 2-D form. The advantage of the method relies on its relatively simple algorithm and easy computational implementation for non-stationary EEG signals. The proposed method is evaluated on publicly available data sets. Smaller error rates were obtained compared to the state-of-the-art methods. The proposed method has potentials to classify EEG patterns and can assist in the development of effective EEG-based BCIs. Abstract: Background and Objective: Electroencephalography (EEG) is a method that measures and records the electrical activity of the human brain. These biomedical signals are currently being actively used in many research fields and have a wide range of potential uses in brain–computer interfaces (BCIs). The main aim of the present work is to improve the classification of EEG patterns for EEG-based BCI systems. Methods: In this paper, we presented a classification approach for EEG-based BCIs. For this purpose, in the training stage, 2-D representations of signals were extracted and a quasi-probabilistic learning model was built for binary classification. In the testing stage, the estimation of class membership probability was performed with an untrained sub-data set. To confirm the validity of the proposed method, we conducted experiments on the BCI Competition 2003 Data Sets (Ia and Ib). The classification performances were evaluated for accuracy, sensitivity, specificity and F-measure measurements using the five-fold leave-one-out cross-validation technique ten times. Results: The proposed method yielded an average classification accuracy of 95.54% (with sensitivity and specificity of 100.00% and 91.80% respectively) for Data Set Ia and accuracy of 72.37% (with sensitivity and specificity of 75.76% and 69.77% respectively) for Data Set Ib, which are the highest rates ever reported for both data sets. Conclusions: It is apparent from the results that the proposed method has potential and can assist in the development of effective EEG-based BCIs. The advantage of this method lies in its relatively simple algorithm and easy computational implementation. The experimental results also showed that the selection of relevant channels is an important step in developing efficient EEG-based BCI systems.