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

Summative EEG-based Assessment of the Relations between Learning Styles and Personality Traits of Openness

AbstractLearning styles (LS), being one of the important attributes of a learner's profile, are relevant to different aspects of teaching and learning such as the learner's achievement and motivation. Equally important is the personality traits of ‘Openness’, which relate positively to knowledge and skill acquisition, thus making them relevant to learning and learners differences. Recognizing the importance of LS and Openness in profiling learners, the researchers carried out this study to examine the relationship between these two factors using a novel method based on Electroencephalogram (EEG) technology. In this research, Kolb's Learning Style Inventory (KLSI) was used to determine 131 participants’ LS: Diverger, Assimilator, Converger or Accommodator. The EEG technology was used to record the participants’ brain signals (with their eyes closed) to generate the dataset of EEG Beta band of baseline condition. Later, the dataset was processed and classified based on the LS using the 2-Step Cluster Analysis. The result showed that the brain signals could be processed effectively to classify the participants’ LS. More importantly, among the LS studied, convergers and assimilators were observed to have positive and strong relation with Openness. Between the two learning styles, assimilators were found to have stronger relation with Openness than convergers

Development of EEG-based stress index

This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%

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

EEG Spectrogram Classification Employing ANN for IQ Application

The term intelligence is associated in many areas such as linguistic, mathematical, music and art. In this paper, Intelligence Quotient (IQ) is measured using Electroencephalogram (EEG) from the human brain. The EEG signals are then used to form the spectrogram images, from which a large data of Gray Level Co-occurrence Matrix (GLCM) texture features were extracted. Then, Principal Component Analysis (PCA) is used to reduce the big matrix, and is followed with the classification of the EEG spectrogram image in IQ application using ANN algorithm. The results are then validated based on the concept of Raven's Standard Progressive Matrices (RPM) IQ test. The results showed that the ANN is able to classify the EEG spectrogram image with 88.89% accuracy and 0.0633 MSE

A Study on the Application of Electronic Nose Coupled with DFA and Statistical Analysis for Evaluating the Relationship between Sample Volumes versus Sensor Intensity of Agarwood Essential Oils Blending Ratio

The exquisite agarwood oils are primary used for perfumery industries either as pure essential oils or in a perfume base. Commonly, Agarwood oils are extracted from low grade 100% agarwood chips via distillation processes and the extracted oil is called as pure agarwood essential oil which containing 100% of extracted material. In perfumery industry, the agarwood pure oils are often blend with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang to create rich, complex and pleasant oils compared to pure Agarwood oils smell alone that may not suit all users preferences. To dates, agarwood oil quality assessment is typically carried out manually via human olfactory system which produces different results and inconsistency from traders and buyers. From the results, multiple linear regression analysis used to run the multiple regression prediction models using combination of 11 sensors shown better results by increasing the R2 value from 0.674 to 0.915 and the RMSE value from 14.65% to 6.80% compared to single regression prediction models using sensor LY2/G. The sensors intensity values from multiple sensors are showing a strong correlation to the volume of the B1 in the blended samples (M11~M20) as the ratio of B1 is increased

A Study on the Application of Electronic Nose Coupled with DFA and Statistical Analysis for Evaluating the Relationship between Sample Volumes versus Sensor Intensity of Agarwood Essential Oils Blending Ratio

The exquisite agarwood oils are primary used for perfumery industries either as pure essential oils or in a perfume base. Commonly, Agarwood oils are extracted from low grade 100% agarwood chips via distillation processes and the extracted oil is called as pure agarwood essential oil which containing 100% of extracted material. In perfumery industry, the agarwood pure oils are often blend with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang to create rich, complex and pleasant oils compared to pure Agarwood oils smell alone that may not suit all users preferences. To dates, agarwood oil quality assessment is typically carried out manually via human olfactory system which produces different results and inconsistency from traders and buyers. From the results, multiple linear regression analysis used to run the multiple regression prediction models using combination of 11 sensors shown better results by increasing the R2 value from 0.674 to 0.915 and the RMSE value from 14.65% to 6.80% compared to single regression prediction models using sensor LY2/G. The sensors intensity values from multiple sensors are showing a strong correlation to the volume of the B1 in the blended samples (M11~M20) as the ratio of B1 is increased

Electroencephalogram-Based Stress Index

Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able to indicate the stress level of healthy human from their brain electrical activity; Electroencephalogram (EEG) signals. This study proposes stress index as an indicator of stress level using EEG signals. The study employs nonparametric method to extract stress features from EEG signals after performing two tasks; do nothing and answer Intelligence Quotient (IQ) test questions. The k-Nearest Neighbor (k-NN) classifier is used to identify the stressed group using the extracted stress features. The results of the study established 3 type of indexes which represent the stress levels (Low Stress, Moderate Stress, High Stress) with 88.89% overall classification accuracy, 86.67% classification sensitivity and 100% classification specificity. The 10-fold and leave-one-out cross validation of the classifier produced classification accuracy of 78.89% and 83.50% respectively

Novel Methods for Stress Features Identification using EEG Signals

This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified and classified using k-Nearest Neighbor (k-NN). The RER in term of Energy Spectral Density (ESD) for each frequency band (delta, theta, alpha and beta) in four different groups consisted of 180 EEG data were calculated and analyzed. Then, the SE was used to confirm the pattern of stress features. Meanwhile, SC was applied to the RER of each group and then the results were selected as input features to k-Nearest Neighbor (k-NN) for the classification purposes. The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The proposed method showed promising results where the combination of RER, SE and SC techniques with the training and testing of k-NN set at 70:30 able to detect and classify the group with the unique stress features at 88.89% accurac