Brain Dominance Using Brainwave Signal

The study of brain dominance in human-computer interaction has increased in recent years in an attempt to address the need of users especially who cannot read or write. The objective of this paper is to determine the brain dominance from brainwave signal that are measured using Emotive device and to analyse the pattern of brain dominance brainwave signal by using signal processing. The result of Power Spectral Density (PSD) and Energy Spectral Density (ESD) from brainwave will be validated with Hermann Brain Dominance Instrumentation (HBDI) questionnaire. The result shows that most sample are left brain dominance. The result also shows that Beta and Delta indicate the left-brain dominance whereas Beta is indicates rightbrain dominance

K-NN Classification of Brain Dominance

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%

K-NN classification of brain dominance

The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%

Human Brain Anatomy: Prospective, Microgravity, Hemispheric Brain Specialisation and Death of a Person

Central nervous system seems to float inside a craniospinal space despite having miniscule amount of CSF. This buoyancy environment seems to have been existing since embryogenesis. This indicates central nervous system always need microgravity environment to function optimally. Presence of buoyancy also causes major flexure to occur at midbrain level and this deep bending area of the brain, better known as greater limbic system seems to regulate brain functions and site for cortical brainwave origin. These special features have made it as a possible site for seat of human soul and form a crucial part in discussion related to death. Besides exploring deep anatomical areas of the brain, superficial cortical areas were also studied. The brainwaves of thirteen clinical patients were analysed. Topographical, equivalent current dipoles and spectral analysis for somatosensory, motor, auditory, visual and language evoked magnetic fields were performed. Data were further analysed using matrix laboratory method for bilateral hemispheric activity and specialization. The results disclosed silent word and picture naming were bilaterally represented, but stronger responses were in the left frontal lobe and in the right parieto-temporal lobes respectively. The sensorimotor responses also showed bilateral hemispheric responses, but stronger in the contralateral hemisphere to the induced sensation or movements. For auditory-visual brainwave responses, bilateral activities were again observed, but their lateralization was mild and could be in any hemisphere. The conclusions drawn from this study are brainwaves associated with cognitive-language, sensorimotor and auditory-visual functions are represented in both hemispheres; and they are efficiently integrated via commissure systems, resulting in one hemispheric specialization. Therefore, this chapter covers superficial, integrative and deep parts of human brain anatomy with emphasis on brainwaves, brain functions, seat of human soul and death

Classification of Brainwave Asymmetry Influenced by Mobile Phone Radiofrequency Emission

AbstractA discriminant classification of human brainwave signals influenced by mobile phone radiofrequency (RF) emission is proposed in this paper. Brainwave signals were recorded using electroencephalograph (EEG) focusing on the alpha sub-band with frequency range from 8 to 12Hz. The EEG test was divided into 3 sessions; Before, During and After with 5minutes duration for each session. Analysis involved 95 participants from engineering students. The students were grouped into 3 groups according to the side of exposure; Left Exposure (LE), Right Exposure (RE) and Sham Exposure (SE). This work suggested that RF emit by the mobile phone give several effects to brainwave signals and there are significant different between the session of exposure. As result, the highest classification rate as high as 94.7% is achieved in session During

Entrainment Promoting Robot Based on Physiological Signals towards Human Cooperation

東京都立大学Tokyo Metropolitan University博士（情報科学）doctoral thesi

Brainwave nets: Are sparse dynamic models susceptible to brain manipulation experimentation?

© Copyright © 2020 Nascimento, Pinto-Orellana, Leite, Edwards, Louzada and Santos. Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity

The Influence on Cortical Brainwaves in Relation to Word Intelligibility and ASW in Room

The influence of indoor speech intelligibility and apparent source width (ASW) on the response of cortical brainwaves was studied using two variables, the time gap between direct and the first reflection (Δt1, ms) and the initial (<80 ms) interaural cross-correlation function (IACCE3). Comparisons were performed based on autocorrelation function (ACF) of continuous brainwave (CBW) and slow vertex response (SVR). The results are: (1) the effective delay time of ACF (τe) of β-waves (13–30 Hz) in the left hemisphere under changes in Δt1 was significantly and positively correlated with speech intelligibility (p < 0.001). (2) As ASW increased, the relative amplitude of left hemisphere A (P2-N2) tended to decrease (p < 0.05) in SVRs, while N2 latency tended to increase (p < 0.05); the lateral lemniscus in the auditory nerve was suggested to be the reactive site. (3) With regard to hemispheric specialization in brain, speech intelligibility, the main temporal factor, was found to be controlled by the left hemisphere. A subjective spatial factor, ASW, the relative amplitude of SVR was also found to decrease in the left hemisphere; nevertheless, they are coherent while the N2 latency of SVR significantly prolonged in both left and right hemisphere under changes in IACCE3

Statistical Analysis of Balanced Brain and IQ Applications

EEG signal research had been studied massively in such balanced brain and IQ applications. This paper focuses on correlation between balanced brain and Intelligence Quotient (IQ) applications. At first, the raw EEG signals from both applications need to pre-process to remove artefact and unwanted frequency. Then, the EEG signals will go through statistical processes which are Scatterplot and Correlation test. As a result, there is correlation between the balanced brain and IQ application with strong and significant Pearson correlation