Norizam Sulaiman

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.

Evaluation of human stress using EEG Power Spectrum

This paper presents an evaluation conducted between human stress questionnaires with EEG Power Spectrum of Beta and Alpha band. Cohens Perceived Stress Scale (PSS) was used as stress questionnaires to evaluate human stress. The EEG recording of 13 volunteers were carried out immediately after them answering the stress questionnaires. The scores from the stress questionnaires were calculated and used to figure out its relationship with the ratio of EEG Beta and Alpha band power. The results of the study showed the PSS was negatively correlated with the ratio of EEG Power Spectrum. Besides, the study suggested that it was feasible to use PSS and the ratio of EEG Power Spectrum to determine human stress.

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.

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.

The Analysis of EEG Spectrogram Image for Brainwave Balancing Application Using ANN

The purpose of this paper is to analysis EEG spectrogram image using Artificial Neural Network (ANN) for brainwave balancing application. Time-frequency approach or spectrogram image processing technique is used to analyze EEG signals. The Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from spectrogram image and passed through Principal components analysis (PCA) to reduce the feature dimension. The experimental result shows that ANN was able to analysis EEG spectrogram images with an optimized model in training by varying neurons in the hidden layer, learning rate and momentum.

Initial investigation on alpha asymmetry during listening to therapy music

Several studies had shown that frontal alpha-asymmetry is closely related to emotions and motivation. The present study is to investigate the frontal alpha asymmetry during listening to the therapy musical piece. Electroencephalograph (EEG) was used to investigate the emotions states of relax, tense, sad or not focus. 10 participants were involved in this study. Mean power of both hemispheres was computed and the Asymmetry Relation Ratio (ARR) was calculated. From the calculation, individuals with relatively positive ratio shows more relax than the negative ratio.

Initial investigation of human physical stress level using brainwaves

This paper presents an investigation of a new technique to measure and indicate the level of human physical stress by studying and evaluating the pattern of the brainwaves using Electroencephalogram (EEG). The EEG data is analyzed using an intelligent signal processing techniques in order to indicate the level of the physical stress.

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.