Development of a Hardware Circuit for Real-Time Acquisition of Brain Activity Using NI myDAQ

2020 ◽  
Vol 29 (10) ◽  
pp. 2050170
Author(s):  
Oinam Robita Chanu ◽  
R. Kalpana ◽  
B. Soorya ◽  
R. Santhosh ◽  
V. Karthik Raj
Keyword(s):  
Electrical Activity ◽  
Virtual Instrument ◽  
Single Channel ◽  
Electrode Placement ◽  
Eeg Signal ◽  
Brain Waves ◽  
Theta Waves ◽  
Patient Preparation ◽  
Alpha Beta ◽  
The Brain

Electroencephalography (EEG) is the recording of electrical activity of the brain. The 10–20 system is the standard electrode location method used to acquire EEG data, which uses 21 electrodes to record the electrical activity of the brain. Patient preparation and correct electrode placement are important to obtain reliable outputs. The current 10–20 system consumes greater time for patient preparation and also causes discomfort due to a higher number of electrodes being used or wearing an uncomfortable cap. This paper focuses on reducing the number of electrodes, thus reducing patient discomfort as well as preparation time. Advancement in the field of hardware and software processing has led to the utilization of brain waves for communication between human and the computer. This work deals with EEG-based Brain–Machine Interface (BMI) intended for designing a portable single-channel EEG signal acquisition system. EEG signal was acquired using the data acquisition module [National Instruments (NI) myDAQ] and the signal was viewed in the NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) environment. It was observed that the peak-to-peak amplitude of alpha, beta and theta waves changes in accordance with the activity the subjects performed. Thus, the developed instrument was tested on 10 different subjects to acquire the alpha, beta and theta waves by performing different activities. From the results, it can be concluded that the developed system can be used for studying a person’s brain waves (alpha, beta and theta) based on the activity performed by the subject with a limited number of electrodes.

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).

Screen filtration pressure of homologous and heterologous blood and electroencephalogram

1964 ◽  
Vol 206 (4) ◽  
pp. 811-814 ◽  
Author(s):  
Hans Hirsch ◽  
Roy L. Swank ◽  
Marianne Breuer ◽  
Wolfgang Hissen
Keyword(s):  
Blood Flow ◽  
Electrical Activity ◽  
Blood Cells ◽  
Perfusion Pressure ◽  
Glass Wool ◽  
Brain Capillaries ◽  
Perfusion Rate ◽  
Brain Waves ◽  
Filtration Pressure ◽  
The Brain

The character and duration of electrical activity arising from a completely isolated cat's head was dependent upon the screen filtration pressure (SFP) of the heparinized oxygenated blood with which it was perfused. If the SFP was above normal the amplitude and frequency of the EEG first decreased, then the brain waves disappeared. The duration of time from the beginning of perfusion until these changes in the EEG occurred was inversely related to the SFP of the perfused blood. Also, the perfusion rate was inversely related to the SFP provided the perfusion pressure remained the same. It is believed that the increase in SFP and changes in EEG and blood flow were due to the presence in the blood of aggregates of blood cells (platelets and leucocytes) which obstructed the brain capillaries. It would appear that heterologous (dog) as well as homologous (cat) blood can be effectively used to perfuse the isolated cat's head provided the blood has a normal SFP. In practice, this was made possible by filtering the blood continuously through Pyrex glass wool.

Selected aspects of people behavior under tests using the VR class driving simulator

2019 ◽  
Vol 125 ◽  
pp. 113-125
Author(s):  
Stanisław Nader
Keyword(s):  
Electrical Activity ◽  
Driving Simulator ◽  
Test Scenario ◽  
Brain Waves ◽  
Questionnaire Research ◽  
Car Driving ◽  
The Brain ◽  
Behavioral Analytics

The paper presents selected results of tests, carried out on a selected group of people using a car driving simulator based on the VR technology. For the prepared test scenario, tests were conducted for a group of 31 people, different in terms of sex and age. In order to analyze the reaction, the behavior of the participants under study using the camera was recorded, and the activity of selected areas of the brain was evaluated using the BCI interface. The analyzed population of people was analyzed for the presence of behavioral analytics during the conducted research, and the reactions of electrical activity in the form of registered brain waves were done. Additionally, questionnaire research was carried out among the respondents, allowing to determine the suitability of the VR technology used in the aspect of realism of the observed sensations and reactions of the subjects.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

FEATURE EXTRACTION OF ELECTROENCEPHALOGRAM SIGNAL GENERATED FROM WRITING IN DYSLEXIC CHILDREN USING DAUBECHIES WAVELET TRANSFORM

2016 ◽  
Vol 78 (6-8) ◽  
Author(s):  
Zulkifli Mahmoodin ◽  
Wahidah Mansor ◽  
Lee Yoot Khuan ◽  
Noor Bariah Mohamad ◽  
Sariah Amirin
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Right Hemisphere ◽  
Electrode Placement ◽  
Daubechies Wavelet ◽  
Eeg Signal ◽  
Beta Band ◽  
Reading And Writing ◽  
The Right ◽  
The Brain

Dyslexia which causes learning deficiencies in reading and writing is due to a neurological disorder where the brain processes information differently. This paper describes the feature extraction of (EEG) signal using Daubechies wavelet transform. The EEG signals were recorded from capable and poor dyslexic children during writing activities of non-words. Brain learning pathway theories for reading and writing were used to localize electrode placement to 8 positions, namely C3, C4, P3, P4, T7, T8, FC5 and FC6. Daubechies provide the wavelet function shape that represent the type of features in an EEG signal well, detecting variations in frequencies that corresponds to activation of areas in relation to activities. Results showed that capable dyslexic subjects exhibit higher beta band power feature of the frontal (FC6) and parietal (P4) right hemisphere if compared to poor dyslexics, where the normal left hemisphere processing center was utilized. This indicates that the brain of dyslexic is compensating its deficiencies of the left brain with activation of areas to the right.  

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.

The Analysis of the Different Frequencies Sound Waves Effect on the EEG Signal

2013 ◽  
Vol 208 ◽  
pp. 177-182 ◽  
Author(s):  
Agata Nawrocka ◽  
Karolina Holewa
Keyword(s):  
Electrical Activity ◽  
Signal Spectrum ◽  
Eeg Signal ◽  
Sound Waves ◽  
Treatment Methods ◽  
Specific Frequency ◽  
The Brain

The aim of the experiment was to study the brain's response to the sound stimuli at a specific frequency. The relation between the specific sounds and the changes in the electrical activity of the brain, caused by it, may be helpful to develop the treatment methods of the various disorders of the brain, using the sounds. The aim of the experiment described in the article is to determine the changes in the signal spectrum during the sounds performance with different parameters and to relate it to the basic rhythms, occurring in the EEG signal.

Fit Vs Faint: Assessing Work Causation in “Idiopathic Fall” Cases

2014 ◽  
Vol 19 (5) ◽  
pp. 3-12
Author(s):  
Lorne Direnfeld ◽  
David B. Torrey ◽  
Jim Black ◽  
LuAnn Haley ◽  
Christopher R. Brigham
Keyword(s):  
Blood Flow ◽  
Electrical Activity ◽  
Loss Of Consciousness ◽  
Brain Electrical Activity ◽  
Medical Terminology ◽  
Scientific Analysis ◽  
Medical Causation ◽  
The Individual ◽  
The Brain ◽  
Current Science

Abstract When an individual falls due to a nonwork-related episode of dizziness, hits their head and sustains injury, do workers’ compensation laws consider such injuries to be compensable? Bearing in mind that each state makes its own laws, the answer depends on what caused the loss of consciousness, and the second asks specifically what happened in the fall that caused the injury? The first question speaks to medical causation, which applies scientific analysis to determine the cause of the problem. The second question addresses legal causation: Under what factual circumstances are injuries of this type potentially covered under the law? Much nuance attends this analysis. The authors discuss idiopathic falls, which in this context means “unique to the individual” as opposed to “of unknown cause,” which is the familiar medical terminology. The article presents three detailed case studies that describe falls that had their genesis in episodes of loss of consciousness, followed by analyses by lawyer or judge authors who address the issue of compensability, including three scenarios from Arizona, California, and Pennsylvania. A medical (scientific) analysis must be thorough and must determine the facts regarding the fall and what occurred: Was the fall due to a fit (eg, a seizure with loss of consciousness attributable to anormal brain electrical activity) or a faint (eg, loss of consciousness attributable to a decrease in blood flow to the brain? The evaluator should be able to fully explain the basis for the conclusions, including references to current science.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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