A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals
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RESEARCH ARTICLE
A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals Ebru Ergu¨n1 • Onder Aydemir2 Received: 28 January 2020 / Revised: 31 March 2020 / Accepted: 11 April 2020 Springer Nature B.V. 2020
Abstract Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user’s intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. In this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes. In this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. In addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. The achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. The results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task. Keywords Brain computer interface Electroencephalography Preprocessing Evolutionary approach Fusion method Feature extraction Classification
Introduction The brain is the main control center of the human body. This control occurs when millions of nerve cells (neurons) that form the structural units of the central nervous system communicate with each other. The flow of information occurs throughout the brain and can be mapped via brain monitoring techniques such as electroencephalography (EEG), electrocorticography (ECoG),
& Ebru Ergu¨n [email protected] 1
Department of Electrical-Electronics Engineering, Faculty of Engineering, Recep Tayyip Erdogan University, Rize, Turkey
2
Department of Electrical-Electronics Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon, Turkey
the magnetoencephalogram (MEG), functional magnetic resonance imaging (fMRI) (Aydemir and Kayikcioglu 2014; Forsyth et al. 2018). EEG is the most widely used monitoring technique through brain computer interfacing (BCI) researchers due to the advantages of high temporal resolution, non-invasiveness, cost-effectiveness and easy portability (Clarke et al. 2016) Researchers
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