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L-theanine acid is an amino acid in tea which affects
mental state directly. Along with other most popular tea types;
white, green, and black tea, Oolong tea also has sufficient Ltheanine
to relax the human brain. It apparently can reduce the
concern, blood pressure, dissolve the fat in the arteries, and
especially slow aging by substances against...
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Objectives:
We have investigated the efficacy of mono- and combined therapy with green tea extract (GTE) in mobilizing redox iron, scavenging reactive oxygen species (ROS), and improving insulin production in iron-loaded pancreatic cells.
Methods:
Rat insulinoma pancreatic β-cells were iron-loaded using culture medium supplemented with either fe...
Citations
... Mind Sound Resonance is a technique in which resonance is produced with the help of mantras. [17] When this resonance occurs, it may affect the brain waves, may be increasing the efficiency of the brain waves and thus affecting the Manomaya Kosha. However, I have yet to see such a study done with an electroencephalography (EEG) device, hence here mentioned only a prediction. ...
The aim of this study is to evaluate the findings of selected articles regarding the effects of the Mind Sound Resonance Technique (MSRT) and to provide a comprehensive review of the benefits of the MSRT. Due to many things such as daily rush, work style, workload, goal fulfillment, increasing stress it creates mental and physical diseases. To prevent these diseases, it is necessary to inform everyone about the effect of MSRT and its benefits. Thus, this manuscript reports on a study of the effects of the MSRT on various group problems and situations. The MSRT is one of the many yoga techniques, which influences the Manomaya Kosha area of the Pancha Kosha. In this technique, use mantra to generate resonance in the body, which mainly works through the Manomaya Kosha to induce deeper relaxation for both mind and body. The results of this study show that practicing the MSRT improves mental health, reduces mental health symptoms, reduces burnout/fatigue, manages occupational stress, increases teacher importance, overcomes mental barriers, improves emotional regulation, and teacher–student relationships as well as increases cognitive performance, reduces anxiety and stress, improves sleep quality, improves mental health, regulates blood pressure, and helps reduce fatigue and pain. Of course, the MSRT works to enhance overall well-being and quality of life.
... Comparison of brain waves[3]. ...
Electroencephalography (EEG) has been used for quite some time as a diagnostic technique in neurology. The goal of this publication is to serve as a resource for researchers interested in applying deep learning methods to EEG data. This paper proposes a unique Hybrid Machine-Deep Learning model that can learn and classify EEG signals on its own. This method allows the model to classify EEG signals of varied sampling frequencies and durations automatically. The proposed model used feature extraction methods from artificial design and performed extensive tests with EEG data collected at varying sample rates to determine how well our suggested model performed. The results show that the Hybrid Machine-Deep Learning strategy significantly improves performance, leading to a remarkable 99.97% classification accuracy. Notably, this method performs exceptionally well when labeling lower-frequency EEG signals (less than 4 Hz). The proposed model has improved consistency and robustness, as shown by this study.
... Brain Waves on frequency, from[33] ...
Metaverse has become a powerful tool for conducting research in many domains, including education, social science, and healthcare. It mixes the virtual and physical environments and can produce various stimuli for users to experience and be immersed in the virtual-real environment. However, at present, these stimuli are preset and immobile, not responding to the user’s changing requirements. In addition, it lacks studies on how brain signals might suggest the demand or preference for specific VR content and if/how VR can interact with users’ brains directly, hands-free, and without verbal instructions. As metaverse’s natural association with learning and brain activities, receiving signals directly from the user’s brain will offer a firm edge to explore mental health issues. This research proposes a new framework, namely Brain-Metaverse Interaction (BMI), which enables the direct interaction between users’ brain signals and the adaptation of VR content in an iterative and evolving manner. Our experiment based on this framework shows promising results, although suffering from the typical limitations of hardware devices and data acquisition, such as signal noise of EEG data and sensitivity and latency of the EEG device.
... Different types of brainwaves[19] ...
Digital technology has absolutely transformed the teaching way in which learning is imparted to students. Unlike campus-based traditional lessons, e-learning makes learning simpler and beneficial for everyone in our society. This article summarizes teaching machines in its history by suggesting possible applications to education and its influence on the human brain. Authors briefly review teaching machines from mechanical to the digital era, describe Pressey and Skinner machines and as well as digital computers. This research also investigates how the human brain is wired to learning and recommends implementing e-learning environments for personal development in the education system. E-learning has become quite popular and appreciated among students and teachers worldwide because of its wide accessible benefits.
... Since the attention and relaxation assessment is required for regular experiments, stable brain signal detector is inevitable. The commercial equipment, Neurosky Mindwave Mobile has been tested for its reliability compared with other equipment in brain signals interpretation [21], this research also takes this efficient and trusty tool for brainwaves detection, accordingly the eSense meter values. This experiment was carried out on group of 10 seniors. ...
... Brainwave Frequencies and Descriptions [12]. ...
Oolong tea has L-theanine acid, which have an impact on human brain wave activity, especially for memorization and meditation. To describe the change in the mentioned states, it requires investigating theta and alpha waves. Higher attention frequency of brain waves indicates an improvement mental activity. Therefore, this study proposes a method to identify the effect level of L-theanine acid in Oo-long tea focused on changing memorization and meditation states individually. It performs the detection of electroencephalography (EEG), statistical analysis, and data classification with specific factors to digest the distinct effect levels. The human brain waves are detected via Neurosky Mindwave Mobile during the book reading state, comparing before drinking and after drinking conditions. The detected signals are converted into voltage values and analyzed a paired sample t-test for defining the difference between the two states statistically. Moreover, an artificial neu-ral network is used to classify the effect level with associated factors including gender, age, and body mass index (BMI) into low, medium, and high levels. It provides 90% accuracy of 17 participants with low, 13 participants with medium, and 6 participants with high levels. The experimental results show that the level of both memorization and meditation is statistically significantly increased after drinking. The instances are presented as scattered data separately. Although L-theanine in Oolong tea increases the mental state, it does not involve the specific factors in this study. Hence, it has different effects on a particular person.
... Fig. 1. NeuroSky Mindwave Mobile [18] It also works with the Neurosky Experimentor application for determining the value of attention and relaxation during the class. The attention meter algorithm of this equipment refers to the intensity of brain focusing, attention value. ...
Human has sustainability to concentrate about 45-50 minutes, approximately. The student who spent a long time during the class without a break is decreasing the brain learning ability. Taking mental breaks every 45 minutes is considered as stress reduction and prepared for better learning. However, a person has a different level to maintain a focus on learning, which is longer or shorter. The well-established information for manipulating this problem is necessary to support the instruction and teaching planning. Therefore, this study proposes the method to define the learning state of each student via brain cognitive performance identification and information technology innovations. The brain signals of students are recorded by electroencephalography (EEG) during studying. Due to the performance values are presented under the specific neuroscience criteria, the Decision Tree algorithm is chosen to perform learning state classification and description. The results present the several levels of cognitive performance including low, neutral, good, and high level, which is related to the learning ability of a student. The student who has low cognitive performance will be noticed to have a mental break before class ends appropriately. The classification method provides 87% of accuracy, which is acceptable to support the implementation of the decision tree with neuroscience in this study.
Significant developments have been made in the field of wearable healthcare by utilizing soft materials for the construction of electronic sensors. However, the lack of adaptability to complex topologies, such as ear canal, results in inadequate sensing performance. Here, we report an in‐ear physiological sensor with mechanical adaptability, which softens upon contact with the ear canal's skin, thus reducing the sensor‐skin mechanical mismatch and interface impedance. An efficient strategy of mechanical adjustment and switching is exploited to increase the softness of the device, leading to a significant decrease in Young's modulus from 30.5 MPa of thermoplastic polyurethane (TPU) to 0.86 MPa of TPU/Ecoflex foam (TEF).The mechanical adaptability at body temperature endows the in‐ear device improved device‐canal contact area and interface stability. As a result, the TEF‐based in‐ear device demonstrates reliable sensing, low motion artifact, and high comfort in electroencephalography (EEG) and core body temperature sensing. High quality EEG signals of alpha, beta, delta, and gamma are measured during different activities. Moreover, the TEF‐based in‐ear device exhibits high reusability for over 4 months, which makes it suitable for long‐term healthcare monitoring.
