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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain...
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Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
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Observing the Transformation of Bodily Self-consciousness in the Squeeze-machine Experiment07:20

Observing the Transformation of Bodily Self-consciousness in the Squeeze-machine Experiment

The squeeze machine, a V-shaped device that provides pressure along the whole body is used in therapy especially for people with autism. Here, we focus on its possible use in experiments to capture the typical transformations of bodily self-consciousness with peri-personal space...
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

This study employed voice signal analysis and machine learning methods, utilizing MATLAB to extract distinctive voice features for non-invasive early detection of asthma. The Support Vector Machine (SVM) and Random Forest (RF) algorithms demonstrated comparable performance in terms of overall classification accuracy, although SVM may achieve a better balance between sensitivity and...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the...
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