A nature-inspired biomarker for mental concentration using a single-channel EEG
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10245016" target="_blank" >RIV/61989100:27240/20:10245016 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007%2Fs00521-019-04574-2" target="_blank" >https://link.springer.com/article/10.1007%2Fs00521-019-04574-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-019-04574-2" target="_blank" >10.1007/s00521-019-04574-2</a>
Alternative languages
Result language
angličtina
Original language name
A nature-inspired biomarker for mental concentration using a single-channel EEG
Original language description
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. (C) 2019, Springer-Verlag London Ltd., part of Springer Nature.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computing and Applications
ISSN
0941-0643
e-ISSN
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Volume of the periodical
32
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
7941-7956
UT code for WoS article
000493504900001
EID of the result in the Scopus database
2-s2.0-85074711492