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Karperien AL, Jelinek HF. Box-Counting Fractal Analysis: A Primer for the Clinician. ADVANCES IN NEUROBIOLOGY 2024; 36:15-55. [PMID: 38468026 DOI: 10.1007/978-3-031-47606-8_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
This chapter lays out the elementary principles of fractal geometry underpinning much of the rest of this book. It assumes a minimal mathematical background, defines the key principles and terms in context, and outlines the basics of a fractal analysis method known as box counting and how it is used to perform fractal, lacunarity, and multifractal analyses. As a standalone reference, this chapter grounds the reader to be able to understand, evaluate, and apply essential methods to appreciate and heal the exquisitely detailed fractal geometry of the brain.
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Affiliation(s)
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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Zhong H, Wang J, Li H, Tian J, Fang J, Xu Y, Jiao W, Li G. Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue. SENSORS (BASEL, SWITZERLAND) 2022; 22:8036. [PMID: 36298387 PMCID: PMC9611295 DOI: 10.3390/s22208036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/18/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.
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Affiliation(s)
- Hongyang Zhong
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Jie Wang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
| | - Huayun Li
- College of Teacher Education, Zhejiang Normal University, Jinhua 321004, China
- Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
| | - Jinghong Tian
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Jiaqi Fang
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Yanting Xu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Weidong Jiao
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua 321004, China
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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Wang F, Lu B, Kang X, Fu R. Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1209. [PMID: 34573834 PMCID: PMC8469593 DOI: 10.3390/e23091209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/10/2021] [Accepted: 09/10/2021] [Indexed: 12/21/2022]
Abstract
The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.
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Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China; (B.L.); (X.K.)
| | - Bin Lu
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China; (B.L.); (X.K.)
| | - Xiaogang Kang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China; (B.L.); (X.K.)
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
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