fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks
Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/7/761 |
| _version_ | 1797562694539149312 |
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| author | Ameer Ghouse Mimma Nardelli Gaetano Valenza |
| author_facet | Ameer Ghouse Mimma Nardelli Gaetano Valenza |
| author_sort | Ameer Ghouse |
| collection | DOAJ |
| description | Conventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis. |
| first_indexed | 2024-03-10T18:32:37Z |
| format | Article |
| id | doaj.art-681d3daba003475cbfee936aa948c6c6 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:32:37Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-681d3daba003475cbfee936aa948c6c62023-11-20T06:30:05ZengMDPI AGEntropy1099-43002020-07-0122776110.3390/e22070761fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic TasksAmeer Ghouse0Mimma Nardelli1Gaetano Valenza2Bioengineering and Robotics Research Center E Piaggio, Università di Pisa, 56123 Pisa, ItalyBioengineering and Robotics Research Center E Piaggio, Università di Pisa, 56123 Pisa, ItalyBioengineering and Robotics Research Center E Piaggio, Università di Pisa, 56123 Pisa, ItalyConventional methods for analyzing functional near-infrared spectroscopy (fNIRS) signals primarily focus on characterizing linear dynamics of the underlying metabolic processes. Nevertheless, linear analysis may underrepresent the true physiological processes that fully characterizes the complex and nonlinear metabolic activity sustaining brain function. Although there have been recent attempts to characterize nonlinearities in fNIRS signals in various experimental protocols, to our knowledge there has yet to be a study that evaluates the utility of complex characterizations of fNIRS in comparison to standard methods, such as the mean value of hemoglobin. Thus, the aim of this study was to investigate the entropy of hemoglobin concentration time series obtained from fNIRS signals and perform a comparitive analysis with standard mean hemoglobin analysis of functional activation. Publicly available data from 29 subjects performing motor imagery and mental arithmetics tasks were exploited for the purpose of this study. The experimental results show that entropy analysis on fNIRS signals may potentially uncover meaningful activation areas that enrich and complement the set identified through a traditional linear analysis.https://www.mdpi.com/1099-4300/22/7/761fNIRSentropycomplexity analysisnonlinear analysisbrain dynamicsmental arithmetics |
| spellingShingle | Ameer Ghouse Mimma Nardelli Gaetano Valenza fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks Entropy fNIRS entropy complexity analysis nonlinear analysis brain dynamics mental arithmetics |
| title | fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks |
| title_full | fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks |
| title_fullStr | fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks |
| title_full_unstemmed | fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks |
| title_short | fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks |
| title_sort | fnirs complexity analysis for the assessment of motor imagery and mental arithmetic tasks |
| topic | fNIRS entropy complexity analysis nonlinear analysis brain dynamics mental arithmetics |
| url | https://www.mdpi.com/1099-4300/22/7/761 |
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