Neuroimaging Modalities

Wang, Shuihua; Zhang, Yudong; Dong, Zhengchao; Phillips, P. Jonathon

doi:10.1007/978-981-10-4026-9_2

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Neuroimaging helps in gathering the structure and function of the brain. Different neuroimaging techniques used for the prognosis of neurological diseases are discussed across many research articles; [2,3] listed various neuroimaging techniques and are mentioned in Table 1.…”

Section: Neuroimaging Modalitiesmentioning

confidence: 99%

Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

Joseph¹,

Jayaraman²

2024

Frontiers in Neuroimaging

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Neuroimage analysis and data processing from various neuro-imaging modalities have been a multidisciplinary research field for a long time. Numerous types of research have been carried out in the area for multiple applications of neuroimaging and intelligent techniques to make faster and more accurate results. Different modalities gather information for detecting, treating, and identifying various neurological disorders. Each modality generates different kinds of data, including images and signals. Applying artificial intelligence-based techniques for analysing the inputs from the neuroimaging modalities requires preprocessing. Preprocessing techniques are used to fine-tune the data for better results and the application of intelligent methods. Various techniques and pipelines/workflows (steps for preprocessing the data from the imaging modalities) have been developed and followed by multiple researchers for the preprocessing of neuroimaging data. The preprocessing steps include the steps followed in removing noisy data from the inputs, converting the data to a different format, and adding additional information to improve the performance of the algorithm on the data. In this chapter, we compare the various neuroimaging techniques, the type of data they generate and the preprocessing techniques that various researchers frequently use to process data to apply them in artificial intelligence-based algorithms for the classification, prediction, and prognosis of various neurological disorders.

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Section: Neuroimaging Modalitiesmentioning

confidence: 99%

Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

Joseph¹,

Jayaraman²

2024

Frontiers in Neuroimaging

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“…Functional Imaging deals with the study using PET, MRI/fMRI, and MEG to study brain functions and the neuroimaging signals that reflect brain activity. 1 EEG is one of the non-invasive and painless techniques to acquire brain activity recordings using sensors, implanted on the scalp. These signals help detect mental health conditions.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary model for sleep quality analytics using fuzzy system

Hangaragi,

Nizampatnam,

Kaliyaperumal

et al. 2023

Proc Inst Mech Eng H

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Electroencephalography (EEG) is a neuro signal reflecting brain activity. These signals provide information about brain activity, eye movements, and muscle tone, which can be used to determine the sleep stage. Categorizing sleep stages can be done manually by visually. Alternatively, automated algorithms can be developed using machine learning techniques to classify sleep stages based on signal features and patterns. This paper aims to automatically classify sleep stages based on extracted patterns from EEG signals. A fuzzy min-max neural network is proposed and implemented for sleep stage classification and clustering. The paper concludes that the fuzzy min-max neural network outperforms other tested methods in sleep stage classification. The models implemented in the study include K-Nearest Neighbor (KNN), Random Forest, Decision Tree, XGBoost, AdaBoost, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN), and the fuzzy min-max classifier. The results indicate that the fuzzy classifier achieves the highest accuracy of 86%, followed by the CNN model with 81%. Among the machine learning algorithms, Random Forest with an accuracy of 55.46%, followed by XGBoost with 53.18%, surpassing the other algorithms used in the experiment. AdaBoost and Gaussian Naive Bayes both achieve an accuracy of 45.10%. Decision Tree, KNN, LDA, and QDA yield accuracies of 37.66%, 16.46%, 28.57%, and 29.5% respectively. These findings demonstrate the efficiency of the fuzzy min-max neural network and the superiority of the fuzzy classifier and CNN models in sleep stage classification, indicating their potential for accurate automated sleep stage analysis.

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Human Brain Imaging for Cognitive Neuroscience

Shaikh

2024

Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases

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In this chapter, a brief background of neuroimaging a human brain by data acquisition and preprocessing is provided. Neuroimaging is a medical imaging process that uses various cutting-edge technologies with artificial intelligence and machine learning to produce a clear and specific image of the brain in a non-invasive manner. Neuroimaging methods such as EEG, CT, and MRI allow researchers to directly observe brain activities from different perspectives. Data acquisition and preprocessing are essential steps in the data analysis and machine learning pipeline. They involve collecting, cleaning, and preparing raw data for further analysis or modeling. These steps are used in noise reduction, sharpening, or brightening an image, and contrast enhancement, color correction, makes it easier to identify the key features. By combining functional brain imaging with sophisticated experimental designs, data analysis methods and machine learning algorithms, functions of brain regions and their interactions can be examined and further how the neurodegenerative diseases are diagnosed.

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Neuroimaging Modalities

Cited by 3 publications

References 30 publications

Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

Preprocessing Techniques for Neuroimaging Modalities: An In-Depth Analysis

An evolutionary model for sleep quality analytics using fuzzy system

Human Brain Imaging for Cognitive Neuroscience

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