scholarly journals Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 293
Author(s):  
Yong-Gi Hong ◽  
Hang-Keun Kim ◽  
Young-Don Son ◽  
Chang-Ki Kang
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Brain Function ◽  
Memory Task ◽  
Mouth Breathing ◽  
Breathing Patterns ◽  
Eeg Signals ◽  
The Difference ◽  
O2 Supply ◽  
The Brain

This study was to investigate the changes in brain function due to lack of oxygen (O2) caused by mouth breathing, and to suggest a method to alleviate the side effects of mouth breathing on brain function through an additional O2 supply. For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O2 supply during a working memory task. The results showed that nose breathing guarantees normal O2 supply to the brain, but mouth breathing interrupts the O2 supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O2 supply. These findings have important implications for the workplace environment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O2 supply is needed in the workplace for working efficiency.

scholarly journals Principles for models of neural information processing

2017 ◽  
Author(s):  
Kendrick N. Kay ◽  
Kevin S. Weiner
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Brain Function ◽  
Network Models ◽  
Neural Network Models ◽  
Specific Effects ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Difference ◽  
The Brain

AbstractThe goal of cognitive neuroscience is to understand how mental operations are performed by the brain. Given the complexity of the brain, this is a challenging endeavor that requires the development of formal models. Here, we provide a perspective on models of neural information processing in cognitive neuroscience. We define what these models are, explain why they are useful, and specify criteria for evaluating models. We also highlight the difference between functional and mechanistic models, and call attention to the value that neuroanatomy has for understanding brain function. Based on the principles we propose, we proceed to evaluate the merit of recently touted deep neural network models. We contend that these models are promising, but substantial work is necessary to (i) clarify what type of explanation these models provide, (ii) determine what specific effects they accurately explain, and (iii) improve our understanding of how they work.

scholarly journals Finding Community Modules of Brain Networks Based on PSO with Uniform Design

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Lingkai Tang ◽  
Bo Liao ◽  
Xiaoshu Zhu ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Function Analysis ◽  
Uniform Design ◽  
Brain Networks ◽  
Complex Structures ◽  
Novel Method ◽  
The Difference ◽  
Living Organisms ◽  
Neural Unit ◽  
The Brain

The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.

Electroencephalagram Analysis in the State of Attention

2013 ◽  
Vol 321-324 ◽  
pp. 716-719
Author(s):  
Jun Chang Zhao ◽  
Zheng Zhong Zheng ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Correlation Analysis ◽  
Working Conditions ◽  
Brain Function ◽  
Cross Correlation ◽  
Brain State ◽  
Eeg Signals ◽  
Number State ◽  
Cross Correlation Analysis ◽  
The Difference ◽  
First Time

Assessment the distinction of different brain working conditions is very important for brain function study. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze different brain working conditions. It were compared the difference of DCCA values for EEG signals under count number state and close eyes state. It was found that the DCCA values of count number state EEG signals decreased compared with close eyes state EEG signals which can be helpful for studying different brain state.

scholarly journals COMPLEXITY-BASED EVALUATION OF THE CORRELATION BETWEEN HEART AND BRAIN RESPONSES TO MUSIC

Fractals ◽  
2021 ◽  
pp. 2150238
Author(s):  
TISARA KUMARASINGHE ◽  
ONDREJ KREJCAR ◽  
ALI SELAMAT ◽  
NORAZRYANA MAT DAWI ◽  
ENRIQUE HERRERA-VIEDMA ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Sample Entropy ◽  
Auditory Stimuli ◽  
Strong Correlations ◽  
Pink Noise ◽  
Eeg Signals ◽  
Brain Responses ◽  
Brown Noise ◽  
The Difference ◽  
The Brain

The evaluation of the correlation between the activations of various organs has great importance. This work investigated the synchronization of the brain and heart responses to different auditory stimuli using complexity-based analysis. We selected three pieces of music based on the difference in the complexity of embedded noise (including white noise, brown noise, and pink noise) in them. We played these pieces of music for 11 subjects (7 M and 4 F) and computed the fractal dimension and sample entropy of EEG signals and R–R time series [as heart rate variability (HRV)]. We found strong correlations ([Formula: see text] in the case of fractal dimension and [Formula: see text] in the case of sample entropy) among the complexities of EEG signals and HRV. This finding demonstrates the synchronization of the brain and heart responses and auditory stimuli from the complexity perspective.

scholarly journals Processing of Affective Pictures: A Study Based on Functional Connectivity Network in the Cerebral Cortex

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongyang He ◽  
Kai Yang ◽  
Ning Zhuang ◽  
Ying Zeng
Keyword(s):  
Cerebral Cortex ◽  
Brain Function ◽  
Middle Temporal Gyrus ◽  
Emotional States ◽  
Eeg Signals ◽  
Brain Areas ◽  
Middle Temporal ◽  
Network Connections ◽  
The Difference ◽  
Function Network

Emotion plays an important role in people’s life. However, the existing researches do not give a unified conclusion on the brain function network under different emotional states. In this study, pictures from the international affective picture system (IAPS) of different valences were presented to subjects with a fixed frequency blinking frequency to induce stable state visual evoked potential (SSVEP). With the source location method, the cerebral cortex source signal was reconstructed based on EEG signals, and then the difference in SSVEP amplitudes in key brain areas under different emotional states and the difference in brain function network connections among different brain areas were analysed in cortical space. The results of the study show that positive and negative emotions evoked greater activation intensities in the prefrontal, temporal, and parietal lobes compared with those of neutral emotion. The network connections with a significant difference between emotional states mainly appear in the alpha and gamma bands, and the network connections with significant differences between positive emotion and negative emotion mainly exist in the right middle temporal gyrus and the superior frontal gyrus on both sides. In addition, the long-range connections play an important role in the process of emotional processing, especially the connections among frontal gyrus, middle temporal gyrus, and middle occipital gyrus. The results of this study provide a reliable scientific basis for revealing and elucidating the neural mechanism of emotion processing and the selection of brain regions and frequency bands in emotion recognition based on EEG signals.

Brain Computations

2020 ◽  
Author(s):  
Edmund T. Rolls
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Brain Function ◽  
Computational Science ◽  
Theoretical Physics ◽  
Medical Sciences ◽  
Computational Approaches ◽  
Potential Applications ◽  
The Subject ◽  
The Brain

The subject of this book is how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed. The book will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.

Machine learning approaches for binary classification using brain signals

2020 ◽  
Author(s):  
Tejas Wadiwala ◽  
Vikas Trikha ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Brain Wave ◽  
Brain Signals ◽  
Learning Practices ◽  
The Brain

<p><b>This paper attempts to perform a comparative analysis of brain signals dataset using various machine learning classifiers such as random forest, gradient boosting, support vector machine, extra trees classifier. The comparative analysis is accomplished based on the performance parameters such as accuracy, area under the ROC curve (AUC), specificity, recall, and precision. The key focus of this paper is to exercise the machine learning practices over an Electroencephalogram (EEG) signals dataset provided by Rochester Institute of Technology and to provide meaningful results using the same. EEG signals are usually captivated to diagnose the problems related to the electrical activities of the brain as it tracks and records brain wave patterns to produce a definitive report on seizure activities of the brain. While exercising machine learning practices, various data preprocessing techniques were implemented to attain cleansed and organized data to predict better results and higher accuracy. Section II gives a comprehensive presurvey of existing work performed so far on the same; furthermore, section III sheds light on the dataset used for this research.</b></p>

scholarly journals Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 411
Author(s):  
Garazi Artola ◽  
Erik Isusquiza ◽  
Ane Errarte ◽  
Maitane Barrenechea ◽  
Ane Alberdi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Prediction Models ◽  
Memory Task ◽  
Alpha Power ◽  
Time Frequency ◽  
Functional Reorganization ◽  
Age Related ◽  
The Brain ◽  
Age Related Changes

Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140–160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain.

Information-Based Analysis of the Coupling Between the Alterations of Heart and Brain Activities in Response to Auditory Stimuli

2021 ◽  
pp. 2150049
Author(s):  
Hamidreza Namazi ◽  
Tisara Kumarasinghe ◽  
Ondrej Krejcar
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Shannon Entropy ◽  
Auditory Stimuli ◽  
Eeg Signals ◽  
Information Contents ◽  
The Difference ◽  
The Brain

In this work, we investigated the coupling among the activities of the brain and heart versus the changes in auditory stimuli using information-based analysis. Three music were selected based on the difference in their complexity. We applied these auditory stimuli on 11 subjects, and accordingly, computed and compared the Shannon entropy of electroencephalography (EEG) signals and heart rate variability (R–R time series). The results demonstrated a correlation among the alterations of the information contents of EEG signals and R–R time series. This finding shows the coupling between the activities of the brain and heart. This analysis could be expanded to analyze the activities of other organs versus the brain’s reaction in various conditions.

scholarly journals Heritability of the human connectome: A connectotyping study

2018 ◽  
Vol 2 (2) ◽  
pp. 175-199 ◽  
Author(s):  
Oscar Miranda-Dominguez ◽  
Eric Feczko ◽  
David S. Grayson ◽  
Hasse Walum ◽  
Joel T. Nigg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Brain Function ◽  
Recent Progress ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Learning Framework ◽  
Novel Approach ◽  
The Brain ◽  
New Strategies

Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity—a “connectotype.” How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.

Sign in / Sign up

Export Citation Format

Share Document