scholarly journals Sacred Buildings and Brain Performance: The Effect of Sultan Hasan Mosque on Brain Waves of its Users

2014 ◽  
Vol 1 (2) ◽  
pp. 125-143 ◽  
Author(s):  
Sally Essawy ◽  
Basil Kamel ◽  
Mohamed Samir
Keyword(s):  
Human Brain ◽  
Wave Frequency ◽  
Brain Wave ◽  
Human Comfort ◽  
Religious Buildings ◽  
Scientific Experiment ◽  
Brain Waves ◽  
State Of Mind ◽  
The Brain ◽  
Positive Effect

A sacred building is defined as a comfortable place that holds certain qualities similar to those originated from nature in harmony with its surroundings. The sacredness quality, though, usually pertains to Religious Buildings that allow for human comfort and a unique state of mind. This paper investigates the effect of sacred buildings on human brain. It concentrates on measurements of brain waves during the presence of the user at certain Paths (coordinates) in these buildings. The variation and intensity of these measurements indicate the effect of “sacredness” as a quality on the user along his/ her journey through the building. This could be used in architecture as evidence of the presence of the sacred quality; and to study the intensity of the positive effect of these buildings. This process is based on a scientific experiment to determine whether or not buildings affect the brain wave frequencies of users, and, measures these effects in terms of Brain Wave Frequency Charts through EEG Devices.

TIMELESS BUILDINGS AND THE HUMAN BRAIN: The Effect of Spiritual Spaces on Human Brain Waves

2014 ◽  
Vol 8 (1) ◽  
pp. 133
Author(s):  
Sally M. Essawy ◽  
Basil Kamel ◽  
Mohamed S. Elsawy
Keyword(s):  
Human Brain ◽  
Case Studies ◽  
Spiritual Experience ◽  
Brain Wave ◽  
Human Comfort ◽  
Beliefs And Practices ◽  
Brain Waves ◽  
Space Design ◽  
State Of Mind ◽  
The Brain

Some buildings hold certain qualities of space design similar to those originated from nature in harmony with its surroundings. These buildings, mostly associated with religious beliefs and practices, allow for human comfort and a unique state of mind. This paper aims to verify such effect on the human brain. It concentrates on measuring brain waves when the user is located in several spots (coordinates) in some of these buildings. Several experiments are conducted on selected case studies to identify whether certain buildings affect the brain wave frequencies of their users or not. These are measured in terms of Brain Wave Frequency Charts through EEG Device. The changes identified on the brain were then translated into a brain diagram that reflects the spiritual experience all through the trip inside the selected buildings. This could then be used in architecture to enhance such unique quality.

scholarly journals Synchronized Activation of Brain Waves by Singing Bowl Beating Sounds

2021 ◽  
Author(s):  
Seong Chan Kim ◽  
Min Joo Choi
Keyword(s):  
Beat Frequency ◽  
Sound Frequency ◽  
Experimental Results ◽  
Wave Frequency ◽  
Brain Wave ◽  
Frequency Bands ◽  
Brain Waves ◽  
Theta Waves ◽  
Singing Bowl ◽  
The Brain

Abstract This study aims to verify if the beating sound of a singing bowl synchronizes and activates brain waves. The singing bowl sound used in this experiment strongly beats at the frequency of 6.68 Hz, while it decays exponentially and lasts for about 50 sec. Brain waves were measured for 5 min at the F3 and F4 region of the 17 subjects who heard the beating singing bowl sounds. Experimental results showed that the increases (up to ~ 251 %) in the spectral magnitudes of the brain waves were dominant at the beat frequency, compared to those of any other clinical brain wave frequency bands. The observed synchronized activation of the brain wave at the beating sound frequency supports that the singing bowl sound may effectively facilitate meditation and relaxation, considering that the beat frequency belongs to theta waves which increases in the relaxed meditation state.

OBSERVATION OF THE EFFECTS OF PLAYING GAMES WITH THE HUMAN BRAIN WAVES

2015 ◽  
Vol 77 (7) ◽  
Author(s):  
Mahfuzah Mustafa ◽  
Rul Azreen Mustafar ◽  
Rosdiyana Samad ◽  
Nor Rul Hasma Abdullah ◽  
Norizam Sulaiman
Keyword(s):  
Human Brain ◽  
Threshold Value ◽  
Band Pass Filter ◽  
Beta Band ◽  
Brain Wave ◽  
Pass Filter ◽  
Brain Waves ◽  
Power Spectral ◽  
Mind State ◽  
Band Pass

The purpose of this paper is to observe the human brain waves when a person playing video games. The game proposed is Counter Strike (CS) 1.6. There are 30 samples of human brain wave will be collected. The EEG signal will be recorded before playing a game and after playing a game. The threshold value is used to filter the data collected to acquire clean brain waves. Then, extraction of sub-band Alpha and Beta is done by Band-pass filter. Power Spectral Density (PSD) is performed in analysing the brain waves to acquire peak amplitude of the Alpha and Beta sub-band frequencies. The pattern of Alpha and Beta is carried out by using the histogram to observe the relationship between games and mind state of humanity. It is observed that the Beta-band increase and Alpha-band decrease after the samples playing game.  

scholarly journals Alvin Lucier's Music for Solo Performer: Experimental music beyond sonification

2014 ◽  
Vol 19 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Volker Straebel ◽  
Wilm Thoben
Keyword(s):  
Performance Practice ◽  
Central Place ◽  
Experimental Music ◽  
Electronic Equipment ◽  
Brain Wave ◽  
Brain Waves ◽  
Percussion Instruments ◽  
History Of ◽  
The Brain

Alvin Lucier's Music for Solo Performer (1965), often referred to as the ‘brain wave piece’, has become a key work of experimental music. Its setup, in which the brain waves of a solo performer are made to excite percussion instruments, has given the work a central place in the discourse on artistic sonification. However, only a small number of the authors making reference to the work seem to have studied the score, and even fewer have given thought to the score's implications for performance practice and aesthetic reflection. This paper pays detailed attention to these yet overlooked aspects, drawing on accounts of early performances as well as the authors’ participation in a 2012 performance led by the composer. We also trace the history of live-electronic equipment used for Music for Solo Performer and discuss the work's reception in sonification research.

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 Boosting Brain Waves Improves Memory

2021 ◽  
Vol 9 ◽  
Author(s):  
Richard J. Addante ◽  
Mairy Yousif ◽  
Rosemarie Valencia ◽  
Constance Greenwood ◽  
Raechel Marino
Keyword(s):  
Wave Pattern ◽  
Brain Wave ◽  
Brain Waves ◽  
Wave Patterns ◽  
The Brain ◽  
With Memory

Have you ever wanted to improve your memory? Or have you struggled to remember what you studied? Memory uses special patterns of activity in the brain. This experiment tested a new way to create brain wave patterns that help with memory. We wanted to see if we could improve memory by using lights and sounds that teach the brain waves to be in sync. People wore special goggles that made flashes of light and headphones that made beeping noises. This trained the brain through a process called entrainment. The entrainment put the brain in sync at a specific brain wave pattern called theta. People whose brains were trained to be in theta had better memory compared to people whose brains did not get trained. We learned that entrainment is a cool new way to make memory better.

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.

scholarly journals Study on EEG Feature Extraction under LED Color Exposure to Enhance the Concentration

2011 ◽  
Vol 2-3 ◽  
pp. 261-265 ◽  
Author(s):  
Jung Eun Lim ◽  
Bo Hyeok Seo ◽  
Sun Hyun Kim ◽  
Soon Yong Chun
Keyword(s):  
Feature Extraction ◽  
Task Performance ◽  
Eeg Signals ◽  
Biometric Data ◽  
Brain Wave ◽  
Brain Waves ◽  
Frequency Domains ◽  
The Brain

Since brain waves are expressed in a variety of frequency domains, they were used to analyze a correlation between colors and concentration. In this study, the brain wave reacting when exposed to colors was defined as a color brain wave (CBW). Also the colors on the table were changed during task performance to see colors’ influence on improving concentration and then the brave waves were measured for analysis on and comparison with the findings from the task performance. Based on the biometric data experiment conducted, it was confirmed that the findings during the task performance and those from EEG signals have a correlation and that human’s concentration is thus affected by changes of colors.

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.

scholarly journals Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities

2019 ◽  
Vol 2 (2) ◽  
pp. 47
Author(s):  
Ahmad Azhari ◽  
Adhi Susanto ◽  
Andri Pranolo ◽  
Yingchi Mao
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Mean Squared Error ◽  
Wave Activity ◽  
Performance Tests ◽  
Brain Wave ◽  
First Order ◽  
Brain Waves ◽  
The Neural Network ◽  
Brain Wave Activity

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994.

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