EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review

2021 ◽  
Vol 43 ◽  
pp. 101329
Author(s):  
Padmavathi Kora ◽  
K. Meenakshi ◽  
K. Swaraja ◽  
A. Rajani ◽  
Mantena Satyanarayana Raju
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Human Brain ◽  
Brain Activity

scholarly journals The Impact of Cannabidiol on Human Brain Function: A Systematic Review

2021 ◽  
Vol 11 ◽  
Author(s):  
Albert Batalla ◽  
Julian Bos ◽  
Amber Postma ◽  
Matthijs G. Bossong
Keyword(s):  
Systematic Review ◽  
Psychiatric Disorder ◽  
Human Brain ◽  
Healthy Volunteers ◽  
Resting State ◽  
Brain Function ◽  
Brain Activity ◽  
Autism Spectrum ◽  
Cognitive Tasks ◽  
Therapeutic Effects

Background: Accumulating evidence suggests that the non-intoxicating cannabinoid compound cannabidiol (CBD) may have antipsychotic and anxiolytic properties, and thus may be a promising new agent in the treatment of psychotic and anxiety disorders. However, the neurobiological substrates underlying the potential therapeutic effects of CBD are still unclear. The aim of this systematic review is to provide a detailed and up-to-date systematic literature overview of neuroimaging studies that investigated the acute impact of CBD on human brain function.Methods: Papers published until May 2020 were included from PubMed following a comprehensive search strategy and pre-determined set of criteria for article selection. We included studies that examined the effects of CBD on brain function of healthy volunteers and individuals diagnosed with a psychiatric disorder, comprising both the effects of CBD alone as well as in direct comparison to those induced by ∆9-tetrahydrocannabinol (THC), the main psychoactive component of Cannabis.Results: One-ninety four studies were identified, of which 17 met inclusion criteria. All studies investigated the acute effects of CBD on brain function during resting state or in the context of cognitive tasks. In healthy volunteers, acute CBD enhanced fronto-striatal resting state connectivity, both compared to placebo and THC. Furthermore, CBD modulated brain activity and had opposite effects when compared to THC following task-specific patterns during various cognitive paradigms, such as emotional processing (fronto-temporal), verbal memory (fronto-striatal), response inhibition (fronto-limbic-striatal), and auditory/visual processing (temporo-occipital). In individuals at clinical high risk for psychosis and patients with established psychosis, acute CBD showed intermediate brain activity compared to placebo and healthy controls during cognitive task performance. CBD modulated resting limbic activity in subjects with anxiety and metabolite levels in patients with autism spectrum disorders.Conclusion: Neuroimaging studies have shown that acute CBD induces significant alterations in brain activity and connectivity patterns during resting state and performance of cognitive tasks in both healthy volunteers and patients with a psychiatric disorder. This included modulation of functional networks relevant for psychiatric disorders, possibly reflecting CBD’s therapeutic effects. Future studies should consider replication of findings and enlarge the inclusion of psychiatric patients, combining longer-term CBD treatment with neuroimaging assessments.

scholarly journals fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

2021 ◽  
Author(s):  
Shuo Huang ◽  
Wei Shao ◽  
Mei-Ling Wang ◽  
Dao-Qiang Zhang
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Visual Information ◽  
Pattern Analysis ◽  
Brain Activity ◽  
Machine Learning Algorithms ◽  
Fmri Data ◽  
Future Research ◽  
Neural Activation ◽  
Research Problems

AbstractOne of the most significant challenges in the neuroscience community is to understand how the human brain works. Recent progress in neuroimaging techniques have validated that it is possible to decode a person’s thoughts, memories, and emotions via functional magnetic resonance imaging (i.e., fMRI) since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions. However, the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools. Given the increasingly important role of machine learning in neuroscience, a great many machine learning algorithms are presented to analyze brain activities from the fMRI data. In this paper, we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects, i.e., brain image functional alignment, brain activity pattern analysis, and visual stimuli reconstruction. In addition, online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.

scholarly journals Using human brain activity to guide machine learning

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ruth C. Fong ◽  
Walter J. Scheirer ◽  
David D. Cox
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Brain Activity

scholarly journals Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity: Toward Its Use as a Signal Feature for Machine Learning in Clinical Applications

2018 ◽  
Vol 9 ◽  
Author(s):  
Lucas G. Souza França ◽  
José G. Vivas Miranda ◽  
Marco Leite ◽  
Niraj K. Sharma ◽  
Matthew C. Walker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Clinical Applications

scholarly journals A Systematic Review of Brain-Computer Interface Based EEG

2020 ◽  
Vol 16 (2) ◽  
pp. 1-10
Author(s):  
Samaa Abdulwahab ◽  
Hussain Khleaf ◽  
Manal Jassim
Keyword(s):  
Systematic Review ◽  
Signal Processing ◽  
Human Brain ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Technical Aspects ◽  
Software And Hardware ◽  
Processing Software

The futuristic age requires progress in handwork or even sub-machine dependency and Brain-Computer Interface (BCI) provides the necessary BCI procession. As the article suggests, it is a pathway between the signals created by a human brain thinking and the computer, which can translate the signal transmitted into action. BCI-processed brain activity is typically measured using EEG. Throughout this article, further intend to provide an available and up-to-date review of EEG-based BCI, concentrating on its technical aspects. In specific, we present several essential neuroscience backgrounds that describe well how to build an EEG-based BCI, including evaluating which signal processing, software, and hardware techniques to use. Individuals discuss Brain-Computer Interface programs, demonstrate some existing device shortcomings, and propose some eld’s viewpoints.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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