Cultivating Chan with Calibration

2015 ◽  
Vol 4 (4) ◽  
pp. 32-51
Author(s):  
Yuezhe Li ◽  
Yuchou Chang ◽  
Hong Lin
Keyword(s):  
Modern Society ◽  
Brain State ◽  
Mental Training ◽  
Eastern Asia ◽  
Machine Learning Methods ◽  
Training Methodology ◽  
Eeg Data ◽  
Brain States ◽  
State Models ◽  
Electroencephalogram Eeg

Chan is a superior mental training methodology derived from Buddhism and absorbed the wisdom of religious practitioners, philosophers, and scholars around Eastern Asia through thousands of years. As the primary way of Chan, meditation has clear effects in bringing practitioners' mind into a tranquil state and promoting both the mental and the physical health. The effect of Chan is measurable. The authors propose to establish a Chan science by applying modern experimental sciences to various models. In particular, machine learning methods have been used to classify brain states using electroencephalogram (EEG) data. The experimental results show potential in building brain state models for calibrating the routine of meditation. Through these studies, the authors believe they will be able to make Chan a beneficial practice to promote human's life in modern society.

Cultivating Chan as Proactive Therapy for Social Wellness

2013 ◽  
pp. 151-170 ◽  
Author(s):  
Hong Lin
Keyword(s):  
Physical Health ◽  
Traditional Medicine ◽  
Human Life ◽  
Modern Society ◽  
Mental Training ◽  
Eastern Asia ◽  
Training Methodology ◽  
Proactive Therapy ◽  
Mental And Physical Health

Chan is a superior mental training methodology derived from Buddhism and absorbed wisdom of religious practitioners, philosophers, and scholars around Eastern Asia through thousands of years. As the primary way of Chan, meditation has clear effects in bringing practitioners’ mind into a tranquil state and promoting both mental and physical health. The effect of Chan is measurable. The authors propose to establish a Chan science by applying modern experimental sciences to various models that have been used in traditional medicine and philosophical studies. Through these studies, they believe they will be able to make Chan a beneficial practice to promote human life in modern society.

Towards a Meditation Brain State Model for Scientific Study of Chan

2013 ◽  
Vol 3 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Hong Lin ◽  
Johnathan Kuskos ◽  
Manuel Palma
Keyword(s):  
Physical Health ◽  
Early Stage ◽  
Analytical Techniques ◽  
Modern Society ◽  
Brain State ◽  
Brain Wave ◽  
Design And Implementation ◽  
Eeg Data ◽  
Brain States ◽  
Psychological Activity

Meditation has clear effects in bringing practitioners’ mind into a tranquil state and promoting both the mental and the physical health. The effect of meditation is measurable. The authors propose to establish a model for meditation state by applying modern experimental sciences to brain wave data. The authors start with a project that aims to create an application that takes electroencephalographic (EEG) data and exposes it to various analytical techniques so the resultant brain states can be studied and predicted. The authors present explanations of the design and implementation offered herein. Furthermore, a summary of the application’s functionality is elucidated. Upon completion, the authors anticipate that this software can be used to produce important and dependable conclusions about a given subject’s brain states and correlate that to an identified physical or psychological activity. Although their project is still in an early stage towards a model for meditation, through these studies, the authors believe they will be able to make meditation a beneficial practice to promote human’s life in modern society.

scholarly journals Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2178
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Chiara Pappalettera ◽  
Alessandro Orticoni ◽  
Francesca Alù ◽  
...  
Keyword(s):  
Chaotic Behavior ◽  
Brain Networks ◽  
Approximate Entropy ◽  
Brain State ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open ◽  
Brain States ◽  
System Disorder ◽  
Electroencephalogram Eeg

Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

scholarly journals Artifacts in EEG-based BCI therapies: friend or foe?

2021 ◽  
Author(s):  
Eric James McDermott ◽  
Philipp Raggam ◽  
Sven Kirsch ◽  
Paolo Belardinelli ◽  
Ulf Ziemann ◽  
...  
Keyword(s):  
Therapeutic Potential ◽  
Brain Activity ◽  
Mental States ◽  
Extraction Methods ◽  
Brain State ◽  
Eeg Signal ◽  
Motor Tasks ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Brain States

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifact component of the EEG signal is significantly more informative than brain activity to the classifier. This finding is consistent across different feature extraction methods and classification pipelines. Whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

scholarly journals Brain State Intelligence and Cognitive Health Through EEG Date Modeling

2021 ◽  
Vol 12 (1) ◽  
pp. 46-61
Author(s):  
Hong Lin ◽  
Jonathan Garza ◽  
Gregor Schreiber ◽  
Minghao Yang ◽  
Yunwei Cui
Keyword(s):  
Data Analysis ◽  
Initial Step ◽  
Machine Learning Algorithms ◽  
Computer Interface ◽  
Brain State ◽  
Data Sets ◽  
Web Interface ◽  
Feedback Systems ◽  
Eeg Data ◽  
Brain States

Electroencephalographic data modeling is widely used in developing applications in the areas of healthcare, as well as brain-computer interface. One particular study is to use meditation research to reach out to the high-end applications of EEG data analysis in understanding human brain states and assisting in promoting human healthcare. The analysis of these states could be the initial step in a process to first predict and later allow individuals to control these states. To this end, the authors begin to build a system for dynamic brain state analysis using EEG data. The system allows users to transit EEG data to an online database through mobile devices, interact with the web server through web interface, and get feedback from EEG data analysis programs on real-time bases. The models perform self-adjusting based on the data sets available in the database. Experimental results obtained from various machine-learning algorithms indicate great potential in recognizing user's brain state with high accuracy. This method will be useful in quick-prototyping onsite brain states feedback systems.

Reduced electroencephalogram complexity in postoperative delirium

2021 ◽  
Author(s):  
Sean Tanabe ◽  
Maggie Parker ◽  
Richard Lennertz ◽  
Robert A Pearce ◽  
Matthew I Banks ◽  
...  
Keyword(s):  
Postoperative Delirium ◽  
Primary Outcome ◽  
Rating Scale ◽  
Monocyte Chemoattractant Protein 1 ◽  
Monocyte Chemoattractant ◽  
Monocyte Chemoattractant Protein ◽  
Eeg Data ◽  
Before And After ◽  
Electroencephalogram Eeg ◽  
Multiple Comparison Correction

Abstract Delirium is associated with electroencephalogram (EEG) slowing and impairments in connectivity. We hypothesized that delirium would be accompanied by a reduction in the available cortical information (i.e. there is less information processing occurring), as measured by a surrogate, Lempil-Ziv Complexity (LZC), a measure of time-domain complexity. Two ongoing perioperative cohort studies (NCT03124303, NCT02926417) contributed EEG data from 91 patients before and after surgery; 89 participants were used in the analyses. After cleaning and filtering (0.1-50Hz), the perioperative change in LZC and LZC normalized (LZCn) to a phase-shuffled distribution were calculated. The primary outcome was the correlation of within-patient paired changes in delirium severity (Delirium Rating Scale-98 [DRS]) and LZC. Scalp-wide threshold free cluster enhancement was employed for multiple comparison correction. LZC negatively correlated with DRS in a scalp-wide manner (peak channel r 2=0.199, p<0.001). This whole brain effect remained for LZCn, though the correlations were weaker (peak channel r 2=0.076, p=0.010). Delirium diagnosis was similarly associated with decreases in LZC (peak channel p<0.001). For LZCn, the topological significance was constrained to the midline posterior regions (peak channel p=0.006). We found a negative correlation of LZC in the posterior and temporal regions with monocyte chemoattractant protein-1 (peak channel r 2=0.264, p<0.001, n=47) but not for LZCn. Complexity of the EEG signal fades proportionately to delirium severity implying reduced cortical information. Peripheral inflammation, as assessed by monocyte chemoattractant protein-1, does not entirely account for this effect, suggesting that additional pathogenic mechanisms are involved.

scholarly journals Theta activity paradoxically boosts gamma and ripple frequency sensitivity in prefrontal interneurons

2021 ◽  
Vol 118 (51) ◽  
pp. e2114549118
Author(s):  
Ricardo Martins Merino ◽  
Carolina Leon-Pinzon ◽  
Walter Stühmer ◽  
Martin Möck ◽  
Jochen F. Staiger ◽  
...  
Keyword(s):  
Low Frequency ◽  
Gamma Oscillations ◽  
Brain State ◽  
Gabaergic Interneurons ◽  
Time Resolved ◽  
Cortical Rhythms ◽  
Brain States ◽  
Fast Spiking ◽  
Fast Oscillations ◽  
Cross Frequency Coupling

Fast oscillations in cortical circuits critically depend on GABAergic interneurons. Which interneuron types and populations can drive different cortical rhythms, however, remains unresolved and may depend on brain state. Here, we measured the sensitivity of different GABAergic interneurons in prefrontal cortex under conditions mimicking distinct brain states. While fast-spiking neurons always exhibited a wide bandwidth of around 400 Hz, the response properties of spike-frequency adapting interneurons switched with the background input’s statistics. Slowly fluctuating background activity, as typical for sleep or quiet wakefulness, dramatically boosted the neurons’ sensitivity to gamma and ripple frequencies. We developed a time-resolved dynamic gain analysis and revealed rapid sensitivity modulations that enable neurons to periodically boost gamma oscillations and ripples during specific phases of ongoing low-frequency oscillations. This mechanism predicts these prefrontal interneurons to be exquisitely sensitive to high-frequency ripples, especially during brain states characterized by slow rhythms, and to contribute substantially to theta-gamma cross-frequency coupling.

scholarly journals Segregated brain state during hypnosis

2019 ◽  
Author(s):  
Jarno Tuominen ◽  
Sakari Kallio ◽  
Valtteri Kaasinen ◽  
Henry Railo
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Brain Activation ◽  
Brain State ◽  
Neural Connectivity ◽  
Single Word ◽  
Electrophysiological Response ◽  
Hypnotic Induction ◽  
Brain States ◽  
The Brain

Can the brain be shifted into a different state using a simple social cue, as tests on highly hypnotisable subjects would suggest? Demonstrating an altered brain state is difficult. Brain activation varies greatly during wakefulness and can be voluntarily influenced. We measured the complexity of electrophysiological response to transcranial magnetic stimulation (TMS) in one “hypnotic virtuoso”. Such a measure produces a response outside the subject’s voluntary control and has been proven adequate for discriminating conscious from unconscious brain states. We show that a single-word hypnotic induction robustly shifted global neural connectivity into a state where activity remained sustained but failed to ignite strong, coherent activity in frontoparietal cortices. Changes in perturbational complexity indicate a similar move toward a more segregated state. We interpret these findings to suggest a shift in the underlying state of the brain, likely moderating subsequent hypnotic responding. [preprint updated 20/02/2020]

scholarly journals Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Laura Cornelissen ◽  
Seong-Eun Kim ◽  
Patrick L Purdon ◽  
Emery N Brown ◽  
Charles B Berde
Keyword(s):  
General Anesthesia ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Body Movement ◽  
Video Recording ◽  
Brain State ◽  
Alpha Power ◽  
State Monitoring ◽  
Spatio Temporal ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) approaches may provide important information about developmental changes in brain-state dynamics during general anesthesia. We used multi-electrode EEG, analyzed with multitaper spectral methods and video recording of body movement to characterize the spatio-temporal dynamics of brain activity in 36 infants 0–6 months old when awake, and during maintenance of and emergence from sevoflurane general anesthesia. During maintenance: (1) slow-delta oscillations were present in all ages; (2) theta and alpha oscillations emerged around 4 months; (3) unlike adults, all infants lacked frontal alpha predominance and coherence. Alpha power was greatest during maintenance, compared to awake and emergence in infants at 4–6 months. During emergence, theta and alpha power decreased with decreasing sevoflurane concentration in infants at 4–6 months. These EEG dynamic differences are likely due to developmental factors including regional differences in synaptogenesis, glucose metabolism, and myelination across the cortex. We demonstrate the need to apply age-adjusted analytic approaches to develop neurophysiologic-based strategies for pediatric anesthetic state monitoring.

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