scholarly journals Attention and Emotional States during Horticultural Activities of Adults in 20s Using Electroencephalography: A Pilot Study

2021 ◽  
Vol 13 (23) ◽  
pp. 12968
Author(s):  
A-Young Lee ◽  
Seon-Ok Kim ◽  
Sin-Ae Park
Keyword(s):  
Pilot Study ◽  
Brain Activity ◽  
Emotional States ◽  
Short Term ◽  
Connection To Nature ◽  
Beneficial Effects ◽  
Brain Waves ◽  
Emotional Changes ◽  
Attention Level ◽  
The Brain

Since indoor, sedentary lifestyles became prevalent in society, humans have lost a sustainable connection to nature. An intervention utilizing outdoor horticultural activities could address such a challenge, but their beneficial effects on the brain and emotions have not been characterized in a quantitative approach. We aimed to investigate brain activity and emotional changes in adults in their 20s during horticultural activity to confirm feasibility of horticultural activity to improve cognitive and emotional states. Sixty university students participated in 11 outdoor horticultural activities at 2-min intervals. We measured brain waves of participants’ prefrontal cortex using a wireless electroencephalography device while performing horticultural activities. Between activities, we evaluated emotional states of participants using questionnaires. Results showed that each horticultural activity showed promotion of brain activity and emotional changes at varying degrees. The participants during physically intensive horticultural activities—digging, raking, and pruning—showed the highest attention level. For emotional states, the participants showed the highest fatigue, tension, and vigor during digging and raking. Plant-based activities—harvesting and transplanting plants—made participants feel natural and relaxed the most. Therefore, this pilot study confirmed the possibility of horticultural activity as a short-term physical intervention to improve attention levels and emotional stability in adults.

scholarly journals Human Emotion Detection using Eeg Sensor

2020 ◽  
Vol 9 (5) ◽  
pp. 409-413
Keyword(s):  
Data Visualization ◽  
Concentration Level ◽  
Brain Activity ◽  
Emotion Detection ◽  
Attention Span ◽  
Brain Waves ◽  
Alpha Beta ◽  
The Right ◽  
The Waves ◽  
The Brain

This is a data visualization art piece using 10 seconds of mind waves recordings of the human, captured with EEG sensor.10 seconds of Alpha, Beta, Gamma & Theta brain waves while meditating are recorded, the different wave channels are categorized to state when the right brain representing artistic brain activity, isolating the ranges for each channel when the brain channels were more meditating and imaginative. Based on the waves of the brain obtained, we will be able to deduce few attributes such as attention span and mood. The moods we will be trying to assess and display here the level of happiness, sadness, anger along with attention span and meditation level (Concentration level).

scholarly journals Fractal Dimension as Quantifier of EEG Activity in Driving Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1311
Author(s):  
Mª Victoria Sebastián ◽  
Mª Antonia Navascués ◽  
Antonio Otal ◽  
Carlos Ruiz ◽  
Mª Ángeles Idiazábal ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Brain Activity ◽  
Fractal Theory ◽  
Driving Simulation ◽  
Geometric Complexity ◽  
Eeg Activity ◽  
Modeling And Analysis ◽  
Brain Waves ◽  
Electroencephalographic Signals ◽  
The Brain

Dynamical systems and fractal theory methodologies have been proved useful for the modeling and analysis of experimental datasets and, in particular, for electroencephalographic signals. The computation of the fractal dimension of approximation curves in the plane enables the assignment of numerical values to bioelectric recordings in order to discriminate between different states of the observed system. The procedure does not require the stationarity of the signals nor extremely long segments of data. In previous works, we checked that this parameter is a good index for brain activity. In this paper, we consider this measurement in order to quantify the geometric complexity of the brain waves in states of rest and during vehicle driving simulation in different scenarios. This work presents evidence that the fractal dimension allows the detection of the brain bioelectric changes produced in the areas that carry out the different driving simulation tasks, increasing with their complexity.

High-Dose Riboflavin as a Prophylactic Treatment of Migraine: Results of an Open Pilot Study

Cephalalgia ◽  
1994 ◽  
Vol 14 (5) ◽  
pp. 328-329 ◽  
Author(s):  
J Schoenen ◽  
M Lenaerts ◽  
E Bastings
Keyword(s):  
Pilot Study ◽  
Side Effects ◽  
Prophylactic Treatment ◽  
Low Cost ◽  
Controlled Trial ◽  
High Dose ◽  
Short Term ◽  
Global Improvement ◽  
The Brain ◽  
Open Pilot

If the brain of migraineurs is characterized between attacks by a reduction of mitochondrial phosphorylation potential, riboflavin, which has the potential of increasing mitochondrial energy efficiency, might have prophylactic effects in migraine. In this preliminary open pilot study, 49 patients suffering from migraine (45 without aura, 4 with aura) were treated with 400 mg of riboflavin as a single oral dose for at least 3 months. Twenty-three patients received in addition 75 mg of aspirin. Mean global improvement after therapy was 68.2% and there was no difference between the two groups of patients. With the exception of one patient in the riboflavin plus aspirin group who withdrew because of gastric intolerance, no drug-related side effects were reported. High-dose riboflavin could thus be an effective, low-cost prophylactic treatment of migraine devoid of short-term side effects. A placebo-controlled trial of its efficacy seems worthwhile.

scholarly journals ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving

2018 ◽  
Vol 6 (3) ◽  
pp. 127
Author(s):  
B. Naresh ◽  
S. Rambabu ◽  
D. Khalandar Basha
Keyword(s):  
Brain Activity ◽  
Traffic Signals ◽  
Electrical Signal ◽  
Emotional States ◽  
Brain Wave ◽  
Computer Interfaces ◽  
Medium Level ◽  
And Control ◽  
To Receive ◽  
The Brain

<span>This paper discussed about EEG-Based Drowsiness Tracking during Distracted Driving based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity commands through controller device in real time. With these signals from brain in mat lab signals spectrum analyzed and estimates driver concentration and meditation conditions. If there is any nearest vehicles to this vehicle a voice alert given to driver for alert. And driver going to sleep gives voice alert for driver using voice chip. And give the information about traffic signal indication using RFID. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human feelings, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) is used to receive the raw data from brain wave sensor and it is used to extract and process the signal using Mat lab platform. The nearest vehicles information is information is taken through ultrasonic sensors and gives voice alert. And traffic signals condition is detected through RF technology.</span>

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

scholarly journals Comparing Concentration Levels and Emotional States of Children Using Electroencephalography during Horticultural and Nonhorticultural Activities

HortScience ◽  
2021 ◽  
pp. 1-6
Author(s):  
Seon-Ok Kim ◽  
Ji-Eun Jeong ◽  
Yun-Ah Oh ◽  
Ha-Ram Kim ◽  
Sin-Ae Park
Keyword(s):  
Power Spectrum ◽  
Brain Activity ◽  
Differential Method ◽  
Emotional States ◽  
University Campus ◽  
School Students ◽  
Folding Paper ◽  
The Right ◽  
Math Problems ◽  
The Brain

This study aimed to compare the brain activity and emotional states of elementary school students during horticultural and nonhorticultural activities. A total of 30 participants with a mean age of 11.4 ± 1.3 years were included. This experiment was conducted at Konkuk University campus in Korea. Participants performed horticultural activities such as harvesting, planting, sowing seeds, and mixing soil. Nonhorticultural activities included playing with a ball, solving math problems, watching animation videos, folding paper, and reading a book. The study had a crossover experimental design. Brain activity of the prefrontal lobes was measured by electroencephalography during each activity for 3 minutes. On completion of each activity, participants answered a subjective emotion questionnaire using the semantic differential method (SDM). Results showed that relative theta (RT) power spectrum was significantly lower in both prefrontal lobes of participants when engaged in harvesting and reading a book. The relative mid beta (RMB) power spectrum was significantly higher in both prefrontal lobes when participants engaged in harvesting and playing with a ball. The ratio of the RMB power spectrum to the RT power spectrum reflects concentration. This ratio increased during harvesting activity, indicating that children’s concentration also increased. The sensorimotor rhythm (SMR) from mid beta to theta (RSMT), another indicator of concentration, was significantly higher in the right prefrontal lobe during harvesting than during other activities. Furthermore, SDM results showed that the participants felt more natural and relaxed when performing horticultural activities than nonhorticultural activities. Horticultural activities may improve brain activity and psychological relaxation in children. Harvesting activity was most effective for improving children’s concentration compared with nonhorticultural activities.

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

The Search for a Survival Value of Music

1984 ◽  
Vol 1 (3) ◽  
pp. 350-356 ◽  
Author(s):  
Juan G. Roederer
Keyword(s):  
Music Perception ◽  
Biological Information ◽  
Speech Communication ◽  
Emotional States ◽  
Short Term ◽  
Current State ◽  
Basic Issue ◽  
Complete Detachment ◽  
The Brain

A most basic issue in the study of music perception is the question of why humans are motivated to pay attention to, or create, musical messages, and why they respond emotionally to them, when such messages seem to convey no real-time relevant biological information as do speech, animal utterances, and environmental sounds. Expanding on previous work (Roederer, 1979,1982) three possibly concurrent factors will be examined: (1) The inborn motivation to train language-handling networks of the brain in the processing of simple, organized sound patterns as a prelude to the acquisition of language; (2) The need to extract the information contained in the "musical" components of speech; (3) The value of music as a means of transmitting information on emotional states and its effect in congregating and behaviorally equalizing masses of people. In the discussion, special attention will be paid to the role of motivation and emotion in auditory perception, to the fact that in humans limbic system functions can be activated by internally evoked images in complete detachment from the current state of environment and organism, and to the existence of two distinct strategies of cerebral information processing, namely short-term time sequencing, as required in speech communication and thinking, and holistic pattern recognition, as required in music perception.

scholarly journals Short-term but not long-term exposure to an enriched environment reduces unconditioned fear responses but not conditioned fear responses

2019 ◽  
Vol 12 (5) ◽  
pp. 86
Author(s):  
K. M. H. Cavalcante
Keyword(s):  
Young Adult ◽  
Environmental Enrichment ◽  
Conditioned Fear ◽  
Enriched Environment ◽  
Spatial Learning And Memory ◽  
Emotional States ◽  
Short Term ◽  
Adult Rats ◽  
Electrical Shock ◽  
Beneficial Effects

Environmental enrichment (EE) has been shown to produce beneficial effects in animal models of a wide variety of neurological and psychiatric disorders. EE exhibits antidepressant function; reduces anxiety, improves spatial learning and memory impairment. EE can reduce sensitivity to loss of reward by reducing frustration-like emotional states and facilitates the extinction of conditioned fear. However, some studies related to the emotional effects of EE present controversial results such as reduction or increase in anxiety. The time of exposure to an enriched environment seems to be an important factor in the behavioral responses presented by animals subjected to aversive stimuli. The present study compared the effects of two and four week exposure to EE with young adult Wistar rats under the same conditions and protocol on fear behavioral parameters in the face of footshock (unconditioned fear) and on re-exposure to an environment after electrical shock pairing (conditioned fear). We showed that the EE with a duration of two weeks reduced the freezing response of the animals in an unconditioned fear situation, that is, with the aversive stimulus present in the environment, however, did not influence the same behavior in a conditioned fear situation. In addition, the short-term EE developed the locomotor and exploratory activity, identified by the high rearing behavior, which may also suggest a low level of anxiety in these animals. We can conclude that EE changes the unconditioned fear responses of young adult rats. In addition, the duration of EE interferes differently, being two weeks of treatment with EE sufficient to cause improvement in coping with unconditioned aversive situations. We suggest that the emotional benefits resulting from the welfare provided by EE can be abolished by the longer duration of this treatment, due to the already known effect of tolerance to lasting or abundant rewards.

scholarly journals Controlling Wheelchair Using Brain as Biosensor

2021 ◽  
Vol 9 (VIII) ◽  
pp. 471-479
Author(s):  
Chandana V
Keyword(s):  
Human Brain ◽  
Brain Activity ◽  
Electrical Signal ◽  
Emotional States ◽  
Brain Wave ◽  
Computer Interfaces ◽  
The Disabled ◽  
Wheel Chair ◽  
And Control ◽  
The Brain

This project discusses about wheel chair controlled by brain based on Brain–computer interfaces (BCI). BCI’s are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. The intention of the project is to develop a robot that can assist the disabled people in their daily life to do some work independent of others. Here, we analyse the brain wave signals. Human brain consists of millions of interconnected neurons, the pattern of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves are sensed by the brain wave sensor and different patterns are used for controlling a wheel chair.

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