N. Fuad

Brainwave sub-band power ratio characteristics in intelligence assessment

This paper discusses on the brainwave sub-band characteristics for different intelligence groups based on electroencephalogram (EEG) power ratio technique. The EEG datasets have been collected from 50 healthy subjects for two sessions; at relaxed, closed eye (CE) state as reference and at cognitively-stimulated state. In the stimulated state, subjects need to answer the intelligence quotient (IQ) test based on Ravens Standard Progressive Matrices (RPM). Sub-band power ratio from the two sessions were calculated and further analyzed to observe the pattern among different IQ groups. The results show that by implementing power ratio technique, the pattern of IQ groups, especially in the relaxed state can be clearly observed. It can be concluded that the value for alpha ratio is higher for high IQ group compared to low IQ group. In contrast to beta and theta ratio where high IQ groups have lower value compared to the low IQ group. This indicates that the ESD ratios can discriminate the characteristic of brainwaves for intelligence assessment.

Evaluation of brainwave sub-band spectral centroid in human intelligence

Sub-band spectral centroid (SC) has been widely applied in audio and speech processing field. This paper highlights the SC feature as a new approach to evaluate human intelligence quotient (IQ). The study focuses on resting EEG of the left brain hemisphere. The SC feature is derived from Discrete Fourier Transform (DFT) of electroencephalogram (EEG) signals. Sub-band SCs are obtained for delta, theta, alpha and beta frequency bands. IQ scores from the Raven Progressive Matrices (RPM) have been utilized to categorize dataset into three distinct groups. The SC features are then evaluated for significant pattern among the different intelligence levels. Results on theta and beta sub-bands indicate a trending pattern. Hence by implementing SC features of the theta and beta sub-bands, distinct IQ groups can be recognized.

Assessment of Brainwave Asymmetry and Hemisphere Dominance Due to RF Radiation

This paper discusses the characteristics of the electroencephalogram (EEG) signals due to the effects of mobile phone RF radiation exposure. The observation is focused on beta and alpha sub-bands. EEG recording was conducted on 66 healthy subjects for 3 sessions, pre, during and post RF radiation with 5 minutes exposure for each session. The subjects were divided into two groups refer to the side of exposure which is Left Exposure (LE) and Right Exposure (RE) group. Power Asymmetry Ratio (PAR) has been applied to determine the brainwave characteristics due to RF radiation. PAR value of the EEG signals in LE and RE group shows a decrement from pre to post RF radiation session. The correlation of PAR beta-alpha signals decrease when comparing between the sessions after exposed to the RF radiation (0.774 to 0.618 in LE and 0.579 to 0.295 in RE).

Classification of intelligence quotient using EEG sub-band power ratio and ANN during mental task

It has been a long debate on conventional psychometric test as benchmark of individuals intelligence quotient (IQ). However, recent studies in a variety of neurophysiological researches have been done to link intelligence with individuals brainwave pattern. Hence this paper proposes an intelligent approach to classify IQ via brainwave sub-band power ratio and artificial neural network (ANN). Fifty samples of electroencephalogram (EEG) dataset have been collected during IQ test session. Three IQ levels have been categorized based on the IQ scores from Ravens Progressive Matrices as the control group. Left hemispheric brainwave focusing on theta, alpha and beta sub-bands are the key discussion of this paper. The features are used as input to train the ANN. Formerly, synthetic data have also been generated with white Gaussian noise to increase the performance of the classifier. Subsequently, the network model have been developed using an ANN that is trained with optimized parameters which are learning rate, momentum constant and hidden neurons. The network model trained with back-propagation algorithm has yielded low mean squared error (MSE). Findings also indicate that the distinct intelligence quotient levels can be classified with 97.62% training and 94.44% testing accuracies via brainwave sub-band power ratio.

Acute ischemic stroke brainwave classification using Relative Power Ratio Discriminant Function Analysis

This study proposes the application of Discriminant Function Analysis (DFA) to classify the brainwaves of stroke patient based on the Relative Power Ratio (RPR) techniques. RPR was performed to determine the brainwave characteristics due to group of stroke level. In this research, hundred stroke patients brainwave activity with open eyes (OE) session were measured, then group into Early Group (EG), Intermediate Group (IG) and Advance Group (AG). The Delta, Theta, Alpha and Beta Power Spectrum Density (PSD) are used as input for RPR. The pattern of group stroke level can be observed especially in the cognitive or thinking abilities by implementing RPR technique. Then DFA was used to predict the outcome to classify RPR towards the corresponding group of stroke level. This indicates that the group stroke level can discriminate due to the characteristics of RPR Delta, Alpha and Delta sub-band.

Brainwave Sub-band Power Spectral Density Characteristics for Human Brain Balanced via Three Dimensional Electroencephalographic Model

This paper discusses on the brainwave sub-band characteristics for different brain balancing index groups based on electroencephalogram (EEG) power spectral density. The EEG datasets have been collected from 51 healthy. There are three groups of brain balancing index; index 3 (moderately balanced), index 4 (balanced) and index 5 (highly balanced). The raw EEG data have done using preprocessing techniques, two dimensional (2D) EEG image development methods and then three dimensional (3D) EEG model development methods. Maximum power spectral density (PSD) was extracted from 3D EEG model. Some analyses done such as Shapiro-Wilk to test normality data, Z score to observe data skewness and Pearson correlation to observe the relationship between sub-band for right and left side. The results show that by implementing maximum PSD, the pattern of brain balancing groups (index 3 to 5) can be clearly observed. The correlation of sub-band for leftright frontal increase when number of index increment. This indicates that the maximum PSD can discriminate the characteristic of brainwaves for brain balancing application.

Three dimension 3D signal for electroencephalographic (EEG)

The present paper examined an experiment of brainwave signal electroencephalographic (EEG) analysis using signal processing and image processing for producing the EEG three dimension (3D) signal. EEG is a scientific tool for measuring brainwaves which give information about brain activity. The EEG signal has been collected from healthy subjects. The proposed method using signal processing for preprocessing stage are threshold, band pass filter and Short Time Fourier Transform (STFT). Threshold algorithm used to artefact removal for EEG raw signal. Band pass filter filtered raw signal into sub bands. STFT has been implemented to get EEG spectrogram. Image processing technique has been implemented to produce EEG 3D signal from EEG spectrogram such as color conversion, optimization, gradient and mesh algorithms. Color conversion has been used to convert from RedGreenBlue (RGB) to gray color and optimization are implemented to gray pixels image. Gradient and Mesh algorithm used to produce the 3D signal. The outcome shows that by implementing 3D signal for EEG, the relationship between three parameters (time, amplitude and power) for brainwave is more clearly.

Brainwave Classification for Acute Ischemic Stroke Group Level Using k-NN Technique

In this study, Relative Power Ratio (RPR) technique is used to analyze the Power Spectral Density (PSD) of the EEG signal. RPR technique is used to observe the difference value of power spectral between sub bands for difference level of group for stroke. In this research, more than hundred stroke patients brainwave activity with open eyes (OE) session are used. Then, they are group into Early Group (EG), Intermediate Group (IG) and Advance Group (AG). The different groups were classified by using k-nearest neighbour (k-NN) method. There are significant different of the EEG signals due to the stroke level. Beta, Alpha, Theta and Delta bands were used as input signals for the classification model. In this study, results from k-NN classification are 100% and 85 % accuracy for training and testing data set respectively. These results proved that k-NN can be used in order to predict the stroke group levels.