Sahrim Lias

EEG-based Stress Features Using Spectral Centroids Technique and k-Nearest Neighbor Classifier

This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) clasifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power spectrum in term of Energy Spectral Density (ESD) for each frequency bands (Delta, Theta, Alpha and Beta) was calculated. The ratio of EEG power spectrum and the average value of Spectral Centroids were selected as features to k-Nearest Neighbor (k-NN). The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The results showed that the combination of EEG power spectrum and Spectral Centroids techniques with the training and testing of k-NN set at 70:30 able to detect and classify the unique features for human stress at 88.89% accuracy.

IQ Index using Alpha-Beta correlation of EEG power spectrum density (PSD)

This paper presents a results of a study to investigate the relationship between Intelligence Quotient (IQ) of humans with their Electroencephalogram (EEG) Spectrum Power in term of the correlation of Beta and Alpha band power. The EEG was recorded from 50 subjects with 21 males and 29 females (mean of age = 23.16, SD = 3.8) for two tasks; closed-eyes (doing nothingin relax state) and IQ test. The results showed that 56% of the subjects have Index 2 while 6% as Index 3 with no Index 1 values. These values conclude that subjects with Alpha-Beta Index 3 and Index 2 is correlated with IQ Index 3(High IQ) and IQ Index 2 (Normal IQ) and there is no correlation between Index 1 for both Indexes.

EEG Analysis for Brainwave Balancing Index (BBI)

The purpose of this research is to establish the fundamental brainwave balancing index (BBI) using EEG signals. Brainwave signals from EEG were measured and analyzed using intelligent signal processing techniques and specific algorithm. Consequently, the signals were statistically correlated with established psychoanalysis techniques to produce BBI system. The result shows that the PSD analysis provides reliable BBI with 80% conformity. The fundamental findings (brainwave balancing index and brain dominance) from this research can be served as a simple indicator of one’s thinking leading to great opportunity for positive human potential development.

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.

Initial investigation of brainwave synchronization after five sessions of Horizontal Rotation intervention using EEG

This research investigates the effects of five sessions of Horizontal Rotation (HR) on human brainwaves synchronization using EEG. EEG signals were captured from 42 participants before and after undergoing HR using two-channel bipolar connection in a controlled environment. The signals were filtered and classified into the four frequency bands; Delta, Theta, Alpha and Beta. Graphs were plotted and paired T-test analysis was used to demonstrate the correlation between left and right brainwaves before and after HR to verify brainwave synchronization. It was observed that after five sessions of HR, brainwaves were more synchronized for all frequency bands with highest increment of 37% in Delta band while the lowest increment is at 6% for Theta band. Thus, there was evidence that HR could synchronize brainwaves.

Feature Extraction of EEG Signals and Classification Using FCM

EEG data were collected between two conditions, relax wakefulness (close-eyes) and non-relax (IQ test). Data segmentation and linear regression model is used to extract the EEG features and to obtain the slope and the mean relative power from 43 participants. All of the data were then normalized and classified using Fuzzy C-Means (FCM) clustering. Results shown that there are different of activities exist in the EEG which proved that the feature extraction using linear regression model manage to discern between two different brain behaviors.

Stress features identification from EEG signals using EEG Asymmetry & Spectral Centroids techniques

This paper presents EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. The study involved 51 subjects (27 males and 24 females) for Close-eye state (do nothing) and 50 subjects (21 males and 29 females) for Open-eye state (perform IQ test). The subjects then were categorized into 2 groups for all EEG frequency bands (Delta, Theta, Alpha and Beta) by using EEG Asymmetry technique. The negative asymmetry was labelled as Stress group and positive asymmetry was labelled as Non-Stress group. The data in each group in term of Energy Spectral Density (ESD) were normalized by using Z-score technique to produce an index to each asymmetry group. Next, the Spectral Centroids techniques were applied to each group and EEG frequency bands to obtain Centroids values. Since there were 2 asymmetry groups per EEG frequency bands, a total of 8 Centroids values were produced for each cognitive states. The plot of Centroids for both cognitive states showed some unique patterns related to stress.

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.

Comparison between the Left and the Right Brainwaves for Delta and Theta Frequency Band after Horizontal Rotation Intervention

This paper investigates the difference between the left and the right brainwaves for delta and theta frequency band after horizontal rotation (HR) intervention using electroencephalography (EEG). The EEG signals were captured from the samples before and after under-going HR. The artifact of the EEG signals was removed automatically by means of a program designed to eliminate data above 100 micro volts. Then the Power Spectral Density (PSD) was applied to the artifact removed data and comparison was carried out to determine the better method and the effects of HR intervention. MATLAB and Paired T-test analysis from SPSS was used and graphs were plotted to show the correlation between the left and right brainwave before and after HR involvement. In conclusion, by applying PSD to the artifact removed signals, results improved significantly. It also shows that the correlation of the left and the right brainwaves had improved for delta and theta frequency bands after HR.

Development of Brainwave Balancing Index Using EEG

In this research, Wireless EEG equipment via Bluetooth technology named g-Mobilab was used to measure the brainwave signals in the right and left frontal area of the brain. The recorded EEG signals were channelled into an automatic artifact removal analysis whereby signals above values of 100 micro-volts were removed by means of a program using Matlab. Consequently, Power Spectral Density techniques and specific algorithm were employed to further enhance the EEG signals. The correlation between the left and the right brainwaves were achieved using paired T test from SPSS. The results, which are brainwave balancing index (BBI) and brainwave dominance, were presented via Graphic User Interface (GUI). The outcome shows that BBI system could be established using EEG signals. These findings (brainwave dominance and BBI) could be used as a straightforward indicator of ones ability to think and work leading to vast opportunity for constructive human potential advancement.