Continuous data analysis of the most important biosignals of the brain—ICP, SAP, CPP, EEG

Memorizing Al-Qur'an is an effort to maintain the purity of the Al-Qur'an. The Qur'an tahfidz program is one of the activities intended to prevent the Qur'an from changing and falsifying either partially or completely. As the name implies, this program is applied to tahfidz Qur'an student to memorize Al-Qur'an under the guidance of their teacher. Memorization ability is determined by memory capacity which indicates brain health, one of which is influenced by the supply of oxygen to the brain. One way to maintain brain oxygenation is the management of deep breathing exercises combined with archery. The purpose of this study was to determine the effect of deep breathing exercises management in archery to achievement of memorizing the Qur'an. This research was held at SMP Ad-Dhuha Jember with a quasi-experimental one group pretest-posttest design method and was conducted in April-June 2019. The type of sample used was a total sampling of 34 respondents. Data analysis used the Wilcoxon Signed-Rank Test. The results showed an increase in the achievement of memorizing Al-Qur'an among students of the tahfidz Qur'an after managing deep breathing exercises with archery. Data analysis with alpha (α) <0.05 indicates a p value of 0.000 so that Ho is rejected. Thus there is an effect of deep breathing exercises management in archery on the achievement of memorizing the Al-Qur'an. A strong memory is needed to keep memorizing Al-Qur'an. To help increase the memory capacity of the brain, it requires an adequate supply of oxygen. Deep breathing exercises management combined with archery can be a way to maintain brain oxygenation. This exercise focuses on fullfiling oxygen needs, which in the process of memorizing the brain's memory functions are widely used.

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Otak dan Akal dalam Kajian Al-Quran dan Neurosains

Jurnal Pendidikan Islam Indonesia ◽

10.35316/jpii.v5i1.242 ◽

2020 ◽

Vol 5 (1) ◽

pp. 67-79

Author(s):

Ahmat Miftakul Huda ◽

Suyadi

Keyword(s):

Data Analysis ◽

Research Data ◽

Data Sources ◽

Analysis Model ◽

Control Center ◽

Model Library ◽

Library Research ◽

Analysis Technique ◽

New Ideas ◽

The Brain

Humans are the perfect creation of Allah SWT. It lies within their mind. Moreover, they also have the brain as a control center for all human activities. This article aims to explain the concepts of al-quran and neuroscience as well as the study of the brain and mind in al-quran and neuroscience. The approach used was qualitative of Creswell model library research. Data sources were obtained from the literature in the fields of the brain and mind, al-quran and neuroscience. Data collection techniques had used Sugiyono model. The data analysis technique had used Moleong analysis model. The results of this study indicated that if humans use their brains and mind to think properly and correctly, they would be able to provide and to create new ideas in solving various problems. After humans are even more aware, they would increase the faith and devotion to Allah.

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Topological Data Analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in Attention-Deficit/Hyperactivity Disorder

10.1101/751008 ◽

2019 ◽

Author(s):

Zeus Gracia-Tabuenca ◽

Juan Carlos Díaz-Patiño ◽

Isaac Arelio ◽

Sarael Alcauter

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Data Analysis ◽

Frontal Lobe ◽

Attention Deficit ◽

Brain Network ◽

Topological Data Analysis ◽

Hyperactivity Disorder ◽

The Brain ◽

Connectivity Threshold ◽

Topological Data

AbstractThe functional organization of the brain network (connectome) has been widely studied as a graph; however, methodological issues may affect the results, such as the brain parcellation scheme or the selection of a proper threshold value. Instead of exploring the brain in terms of a static connectivity threshold, this work explores its algebraic topology as a function of the filtration value (i.e., the connectivity threshold), a process termed the Rips filtration in Topological Data Analysis. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value, in a public dataset of children with attention-deficit/hyperactivity disorder (ADHD) and typically developing children. Results were highly congruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole brain network and at the functional sub-network levels, particularly involving the frontal lobe and the default mode network. Therefore, this approach may contribute to identify the neurophysio-pathology of ADHD, reducing the bias of connectomics-related methods.HighlightsTopological Data Analysis was implemented in functional connectomes.Betti curves were assessed based on the area under the curve, slope and kurtosis.The explored variables were robust along four different brain atlases.ADHD showed lower areas, suggesting decreased functional segregation.Frontal and default mode networks showed the greatest differences between groups.Graphical Abstract

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Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

10.1101/2021.02.13.431051 ◽

2021 ◽

Author(s):

Faezeh Moradi ◽

Shima T. Moein ◽

Issa Zakeri ◽

Kambiz Pourrezaei

Keyword(s):

Infrared Spectroscopy ◽

Data Analysis ◽

Near Infrared Spectroscopy ◽

Functional Data Analysis ◽

Functional Data ◽

Near Infrared ◽

Stimulus Condition ◽

Functional Near Infrared Spectroscopy ◽

Odor Detection ◽

The Brain

AbstractAn objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment.These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

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Artifacts

MEG-EEG Primer ◽

10.1093/med/9780190497774.003.0008 ◽

2017 ◽

pp. 98-127

Author(s):

Riitta Hari ◽

Aina Puce

Keyword(s):

Data Analysis ◽

Eye Movements ◽

Cardiac Activity ◽

Signal Space Separation ◽

Eeg Data ◽

Eeg Recordings ◽

Power Line Noise ◽

Post Hoc ◽

Space Separation ◽

The Brain

This chapter focuses on different types of biological and nonbiological artifacts in MEG and EEG recordings, and discusses methods for their recognition and removal. Examples are given of various physiological artifacts, including eye movements, eyeblinks, saccades, muscle, and cardiac activity. Nonbiological artifacts, such as power-line noise, are also demonstrated. Some examples are given to illustrate how these unwanted signals can be identified and removed from MEG and EEG signals with methods such as independent component analysis (as applied to EEG data) and temporal signal-space separation (applied to MEG data). However, prevention of artifacts is always preferable to removing or compensating for them post hoc during data analysis. The chapter concludes with a discussion of how to ensure that signals are emanating from the brain and not from other sources.

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