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A nature-inspired biomarker for mental concentration using a single-channel EEG

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Abstract

We developed a system for measuring the attentional process during the performance of specific activities. The proposed biomarker device is able to estimate the mental concentration using a single-channel EEG. The system captures the EEG signal and several brain waves located in the left orbitofrontal brain region. Furthermore, we extended the input features of the system applying spectrum analysis. We applied two well-known evolutionary algorithms for selecting the best combination of input features: simulated annealing and geometric particle swarm optimization. Besides, we solved the binary classification problem (concentration vs. relaxation) using support vector machines and neural networks. Support vector machines are among the most common instruments for solving binary classification problems. On the other hand, we selected to study a family of neural networks named echo state networks, because the model is ideal for embedded systems and has shown good accuracy in real-world applications. The training and execution are fast, robust, and reliable. The developed system is autonomous, portable, reliable, non-invasive and has a low economic cost. Besides, it can be easily adjusted for each person and for each problem.

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Acknowledgements

This work was supported by the Technology Agency of the Czech Republic in the frame of the project no. TN01000024 “National Competence Center – Cybernetics and Artificial Intelligence”, and by the projects SP2019/135 and SP2019/141 of the Student Grant System, VSB – Technical University of Ostrava.

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  1. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Sebastián Basterrech & Pavel Krömer

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  1. Sebastián Basterrech
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Correspondence to Sebastián Basterrech.

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Basterrech, S., Krömer, P. A nature-inspired biomarker for mental concentration using a single-channel EEG. Neural Comput & Applic 32, 7941–7956 (2020). https://doi.org/10.1007/s00521-019-04574-2

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