Noor Hayatee Abdul Hamid

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.

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.

Application of PID controller in controlling refrigerator temperature

This paper presents the comparison of performances between a PID temperature controller and a conventional on-off temperature controller for home-applied refrigerator. A new PID temperature controller was designed to replace the conventional on-off temperature controller to maintain the inner temperature of a refrigerator. Both temperature controllers of the conventional system and the PID were designed and evaluated using MATLAB Simulink software. The simulation shows that the PID controller was found to be more consistent and effective in maintaining the inner temperature of the refrigerator despite rapid changes in the outer temperature, than the conventional on-off controller. The both simulation results provide full graphical development which is analyzed and have been compared. The effectiveness of the PID control system and its ability to maintain the inner temperature of the refrigerator consistently than the conventional on-off controller is shown.

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.

Brainwaves stress pattern based on perceived stress scale test

This paper presents human stress pattern for alpha and beta waves obtained from EEG Power Spectrum analysis. The EEG stress evaluation conducted between human Cohens PSS-10 Stress Questionnaires with EEG Power Spectrum. The EEG recording had been carried out among 86 volunteers as soon as they finished answering the stress questionnaires. The scores from questionnaires were computed immediately right after sample answering the questionnaires using GUI. Hence, the total stress score is used to figure out its relationship with the EEG Power Spectrum in determining the left or right brain activation during stress presence. The results of the study showed that there is EEG Human Stress Pattern for Low Stress and High Stress which is associated with Alpha and Beta Waves.

Intelligent System for Assessing Human Stress Using EEG Signals and Psychoanalysis Tests

This paper presents a results of designing an intelligent system to evaluate human stress level using Electroencephalogram (EEG) signals and Psychoanalysis tests. The questionnaires for Psychoanalysis tests were created based on Cohens Perceived Stress Scale (PSS). EEG signals were captured using wireless EEG equipment. The Graphical User Interface (GUI) for the Psychoanalysis tests and EEG signals were created. The system was evaluated for 12 healthy subjects (7 females and 5 males). The results show that the intelligent system able to display the stress score, stress level and dominant index of EEG signals simultaneously. Thus, users can use the results of the system to take necessary action in order to improve their lifestyle.

GLCM texture classification for EEG spectrogram image

Over the past century, time based and frequency based is used for analyzing Electroencephalography (EEG) signals. EEG is a scientific tool for measure signal from human brain. This paper proposes a time-frequency approach or spectrogram image processing technique for analyzing EEG signals. Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted from spectrogram image and then Principal components analysis (PCA) was employed to reduce the feature dimension. The purpose of this paper is to classify EEG spectrogram image using k-nearest neighbor algorithm (kNN) classifier. The result shows classification rate was 70.83% for EEG spectrogram image.

Theta, Alpha and Beta Band frequencies of different nicotine dependency level among smokers

This paper presents a study of EEG pattern for different levels of nicotine dependence for smokers based on Theta, Alpha and Beta Band power using Power Spectral Density (PSD). 33 male smokers participated for this research. Participants were asked to fill in the Fagerstrom Test for Nicotine Dependence Questionnaire and EEG data was recorded for 5 minutes. Results show some difference in the Theta, Alpha and Beta Band power of smokers from different levels of nicotine dependence, where medium dependence smokers have a higher Theta, Alpha and Beta Band power compared to low dependence smokers.