Analysis of the Changes in the Brain Activity Between Rest and Multitasking Workload by Complexity-Based Analysis of Eeg Signals

PARTHASARATHY, SRIRAM; MARESOVA, PETRA; RAJAGOPAL, KARTHIKEYAN; NAMAZI, HAMIDREZA

doi:10.1142/s0218348x23501360

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…It provides in-depth insights into system behavior across different time scales. Currently, approximate entropy has demonstrated effective applications in various fields, including biosignal analysis [ 28 ], short-circuiting arc welding analysis [ 29 ], mechanical vibration measurements [ 30 ], and environmental monitoring [ 31 ]. In transformer diagnosis, this paper attempts to enhance early fault prediction by calculating the multi-scale approximate entropy of dissolved gases in oil, offering a more comprehensive insight into system state changes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

Shang,

Liu,

Wei

et al. 2024

Entropy

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Dissolved gas analysis (DGA) in transformer oil, which analyzes its gas content, is valuable for promptly detecting potential faults in oil-immersed transformers. Given the limitations of traditional transformer fault diagnostic methods, such as insufficient gas characteristic components and a high misjudgment rate for transformer faults, this study proposes a transformer fault diagnosis model based on multi-scale approximate entropy and optimized convolutional neural networks (CNNs). This study introduces an improved sparrow search algorithm (ISSA) for optimizing CNN parameters, establishing the ISSA-CNN transformer fault diagnosis model. The dissolved gas components in the transformer oil are analyzed, and the multi-scale approximate entropy of the gas content under different fault modes is calculated. The computed entropy values are then used as feature parameters for the ISSA-CNN model to derive diagnostic results. Experimental data analysis demonstrates that multi-scale approximate entropy effectively characterizes the dissolved gas components in the transformer oil, significantly improving the diagnostic efficiency. Comparative analysis with BPNN, ELM, and CNNs validates the effectiveness and superiority of the proposed ISSA-CNN diagnostic model across various evaluation metrics.

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Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

Shang,

Liu,

Wei

et al. 2024

Entropy

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Complexity-Based Analysis in Biomedical Image Analysis: A Review

PAKNIYAT,

ABDULLAH,

KREJCAR

et al. 2024

Fractals

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This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such as fractal dimension, entropy measures, and network analysis in characterizing the complexity of biomedical images (e.g. magnetic resonance imaging (MRI), computed tomography (CT) scans, X-ray images). Additionally, we discuss the clinical implications of complexity-based analysis in areas such as cancer detection, neuroimaging, and cardiovascular imaging, highlighting its potential to improve diagnostic accuracy, prognostic assessment, and treatment outcomes. The review concludes that complexity-based analysis significantly enhances the interpretability and diagnostic power of biomedical imaging, paving the way for more personalized and precise medical care. By elucidating the role of complexity-based analysis in biomedical image analysis, this review aims to provide insights into current trends, challenges, and future directions in this rapidly evolving field.

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Analysis of the Effect of Various Mental Tasks on the Eeg Signals’ Complexity

PAKNIYAT,

KREJCAR,

MARESOVA

et al. 2024

Fractals

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Analysis of the brain activity in different mental tasks is an important area of research. We used complexity-based analysis to study the changes in brain activity in four mental tasks: relaxation, Stroop color-word, mirror image recognition, and arithmetic tasks. We used fractal theory, sample entropy, and approximate entropy to analyze the changes in electroencephalogram (EEG) signals between different tasks. Our analysis showed that by moving from relaxation to the Stroop color-word, arithmetic, and mirror image recognition tasks, the complexity of EEG signals increases, respectively, reflecting rising brain activity between these conditions. Furthermore, only the fractal theory could decode the significant changes in brain activity between different conditions. Similar analyses can be done to decode the brain activity in case of other conditions.

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Analysis of the Changes in the Brain Activity Between Rest and Multitasking Workload by Complexity-Based Analysis of Eeg Signals

Cited by 5 publications

References 45 publications

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

A Novel Fault Diagnosis Method for a Power Transformer Based on Multi-Scale Approximate Entropy and Optimized Convolutional Networks

Complexity-Based Analysis in Biomedical Image Analysis: A Review

Analysis of the Effect of Various Mental Tasks on the Eeg Signals’ Complexity

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