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%

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

Classification of EEG spectrogram image with ANN approach for brainwave balancing application

In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis(PCA) is used to reduce the feature dimension. The result shows that the proposed model is able to classify EEG spectrogram images with 77% to 84% accuracy for three classes of brainwave balancing application with an optimized ANN model in training by varying the neurons in the hidden layer, epoch, momentum rate and learning rate

The characteristics of human body electromagnetic radiation frequencies for stroke patients and non-stroke participants

This paper presents the characteristic of human body electromagnetic radiation (EMR) frequencies for the stroke patients and non-stroke participants. 199 subjects undergoing post-stroke treatment and 100 non-stroke participants involved in this research. The human body EMR frequencies (in MHz) is captured using frequency detector at 23 points around their body, namely left side, right side and chakra points. Data analyses were evaluated by looking at the pattern and behavior of the captured frequencies. In conclusion, the characteristic of electromagnetic radiation (EMR) frequencies vary between variables and also between two groups of samples. Higher frequency radiations are detected on the stroke patients as compare to non-stroke participants, hence supporting the assumption that the human physiological conditions influence the body EMR radiation frequencies. This observation also support that EMR from human body can contribute to early detection for stroke

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

Learners' Learning Style classification related to IQ and Stress based on EEG

The importance to recognize a learner's Learning Style (LS) is ever-essential as to substantiate success in a teaching and learning process. At the same time, the learner's IQ and personality traits such as Stress also being actively investigated in educational research as educationists consistently attempted to understand learners in a more adept way. Nevertheless, the effort was usually confined to psychoanalysis test. With the emergence of Electroencephalography (EEG) technology, learner's brain characteristics could be accessed directly and the outcome may well hand-in-hand supported the conventional test. In this study, the participants (n= 80) are grouped to the LS of Diverger, Assimilator, Converger or Accommodator using the Kolb's Learning Style Inventory (KLSI). Subsequently, their brain signals were then recorded using EEG at resting baseline state of Open Eyes and Closed Eyes. A statistical tool of SPSS 16 was used for data analysis purposes. Using the Two Step Cluster analysis, the participants’ EEG datasets were 100% classified to the corresponding LS. Then, EEG Alpha band was selected to link between LS, IQ and Stress. The study concluded that Diverger is the LS with highest IQ while Converger and Diverger are the LS that prone to Stress

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