Determination and classification of human stress index using nonparametric analysis of EEG signals / Norizam Sulaiman

Regardless of type of stress, either mental stress, emotional stress or physical stress, it definitely affects human lifestyle and work performance. There are two prominent methods in assessing stress which are psychological assessment (qualitative method) and physiological assessment (quantitative method). This research proposes a new stress index based on Electroencephalogram (EEG) signals and non-parametric analysis of the signals. In non-parametric method, the EEG features that might relate to stress are extracted in term of Asymmetry Ratio (AR), Relative Energy Ratio (RER), Spectral Centroids (SC) and Spectral Entropy (SE). The selected features are fed to the k-Nearest Neighbor (k- NN) classifier to identify the stressed group among the four experimental groups being tested. The classification results are based on accuracy, sensitivity and specificity. To support the classification results using k-NN classifier, the clustering techniques using Fuzzy C-Means (FCM) and Fuzzy K-Means (FKM) are implemented. To ensure the robustness of the classifier, the cross-validation technique using k-fold and leave-oneout is performed to the classifier

Determination and classification of human stress index using nonparametric analysis of EEG signals / Norizam Sulaiman

Regardless of type of stress, either mental stress, emotional stress or physical stress, it definitely affects human lifestyle and work performance. There are two prominent methods in assessing stress which are psychological assessment (qualitative method) and physiological assessment (quantitative method). This research proposes a new stress index based on Electroencephalogram (EEG) signals and non-parametric analysis of the signals. In non-parametric method, the EEG features that might relate to stress are extracted in term of Asymmetry Ratio (AR), Relative Energy Ratio (RER), Spectral Centroids (SC) and Spectral Entropy (SE). The selected features are fed to the k-Nearest Neighbor (k-NN) classifier to identify the stressed group among the four experimental groups being tested. The classification results are based on accuracy, sensitivity and specificity. To support the classification results using k-NN classifier, the clustering techniques using Fuzzy C-Means (FCM) and Fuzzy K-Means (FKM) are implemented. To ensure the robustness of the classifier, the cross validation technique using k-fold and leave-one-out is performed to the classifier. The assignment of the stress index is verified by applying Z-score technique to the selected EEG features. The experiments established a 3-level index (Index 1, Index 2 and Index 3) which represents the stress levels of low stress, moderate stress and high stress at overall classification accuracy of 88.89%, classification sensitivity of 86.67 % and classification specificity of 100%. The outcome of the research suggests that the stress level of human can be determined accurately by applying SC on the ratio of the Energy Spectral Density (ESD) of Beta and Alpha bands of the brain signals. The experimental results of this study also confirm that human stress level can be determined and classified precisely using physiological signal through the proposed stress index. The high accuracy, sensitivity and specificity of the classifier might also indicate the robustness of the proposed method

Classification of frontal alpha asymmetry using k-Nearest Neighbor

Frontal alpha asymmetry is used as the EEG feature in this study. Total number of 43 students participated in EEG data collections of relax and non-relax conditions. The spectral power of the alpha band for both left and right brain are extracted using data segmentations and then the Asymmetry Score (AS) is computed. Subtractive clustering is used to predetermine the number of cluster center that are presented in the data. While Fuzzy C-Means (FCM), is used to discriminate the EEG data into an appropriate cluster after the total number of cluster had been determined. The classification rate obtained from the k-Nearest Neighbor (k-NN) classifier is 84.62% which gives the highest classification rate

Hiving Method Of Stingless Bee Domestication For Sustainable Meliponiculture

The stingless bee rearing is emergent agriculture activity in Malaysia. The stingless bee products including honey and bee bread, which are stored in propolis-rich pots. The activities of stingless bee rearing are referred to as meliponiculture and it is a crucial activity that encourages the conservation of stingless bees and helping to reduce deforestation due to feral stingless bee colony hunting. Here, we studied direct and indirect hiving method of the feral stingless bee colony into a newly innovative hive called Mustafa-Hive. In the hive, the brood was placed into a split-able throne as a brood chamber and inserted into an air-jacketed palace as an insulation chamber. The honey cassette was used on the hive to induce a monolayer honey pot formation. Findings have shown that all broods in an indirect hiving module provide cleaner broods from the sawdust compared to the direct hiving process. Indiriect hiving gave 100% colony viability and supported by noteworthy yield pot formations in the honeycassette. Findings also showed an average of 4.5ml honey were extracted from each pot to produce an average of 99ml and 256.5ml honey at week 2 and week 4 for every hive, respectively. As conclusion, the indirect hiving method and the use of Mustafa-hive ensures colony survival and induced formation of monolayer honey pots. Thus this hiving module encourage for sustainable meliponiculture, enables for absolute and hygienic honey extractions from honey cassette which could indirectly promote the development of the stingless bee industr

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

Five-Class SSVEP Response Detection using Common-Spatial Pattern (CSP)-SVM Approach

Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological responses including P300, motor imagery, movement-related potential, steady-state visual evoked potential (SSVEP) and slow cortical potential have been utilized. Because of the superior signal-to-noise ratio (SNR) together with quicker information transfer rate (ITR), the intentness of SSVEP-based BCIs is progressing significantly. This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. The common-spatial pattern (CSP) has been employed to extract the features from SSVEP responses and these features have been classified through the support vector machine (SVM). The proposed architecture has achieved the highest classification accuracy of 88.3%. The experimental result proves that the proposed architecture could be utilized for the detection of SSVEP responses to develop any BCI applications

Diagnosis of hearing impairment based on wavelet transformation and machine learning approach

Hearing impairment has become the most widespread sensory disorder in the world, obstructing human-to-human communication and comprehension. The EEG-based brain-computer interface (BCI) technology may be an important solution to rehabilitating their hearing capacity for people who are unable to sustain verbal contact and behavioral response by sound stimulation. Auditory evoked potentials (AEPs) are a kind of EEG signal produced by an acoustic stimulus from the brain scalp. This study aims to develop an intelligent hearing level assessment technique using AEP signals to address these concerns. First, we convert the raw AEP signals into the time–frequency image using the continuous wavelet transform (CWT). Then, the Support vector machine (SVM) approach is used for classifying the time–frequency images. This study uses the reputed publicly available dataset to check the validation of the proposed approach. This approach achieves a maximum of 95.21% classification accuracy, which clearly indicates that the approach provides a very encouraging performance for detecting the AEPs responses in determining human auditory level

Observation of the Effects of Playing Games with the Human Brain Waves

The purpose of this paper is to observe the human brain waves when a person playing video games. The game proposed is Counter Strike (CS) 1.6. There are 30 samples of human brain wave will be collected. The EEG signal will be recorded before playing a game and after playing a game. The threshold value is used to filter the data collected to acquire clean brain waves. Then, extraction of sub-band Alpha and Beta is done by Band-pass filter. Power Spectral Density (PSD) is performed in analysing the brain waves to acquire peak amplitude of the Alpha and Beta sub-band frequencies. The pattern of Alpha and Beta is carried out by using the histogram to observe the relationship between games and mind state of humanity. It is observed that the Beta-band increase and Alpha-band decrease after the samples playing game

Development of wireless vehicle remote control for fuel lid operation

Nowadays, the evolution of the vehicle technology had made the vehicle especially car to be equipped with a remote control to control the operation of the locking and unlocking system of the car’s door and rear’s bonnet. However, for the fuel or petrol lid, it merely can be opened from inside the car’s cabin by handling the fuel level inside the car’s cabin to open the fuel lid. The petrol lid can be closed by pushing the lid by hand. Due to the high usage of using fuel lever to open the fuel lid when refilling the fuel, the car driver might encounter the malfunction of fuel lid (fail to open) when pushing or pulling the fuel lever. Thus, the main aim of the research is to enhance the operation of an existing car remote control where the car fuel lid can be controlled using two techniques; remote control-based and smartphone-based. The remote control is constructed using Arduino microcontroller, wireless sensors and XCTU software to set the transmitting and receiving parameters. Meanwhile, the smartphone can control the operation of the fuel lid by communicating with Arduino microcontroller which is attached to the fuel lid using Bluetooth sensor to open the petrol lid. In order to avoid the conflict of instruction between wireless systems with the existing mechanical-based system, the servo motor will be employed to release the fuel lid merely after receiving the instruction from Arduino microcontroller and smartphone. As a conclusion, the prototype of the multipurpose vehicle remote control is successfully invented, constructed and tested. The car fuel lid can be opened either using remote control or smartphone in a sequential manner. Therefore, the outcome of the project can be used to serve as an alternative solution to solve the car fuel lid problem even though the problem rarely occurred

Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach

Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological responses including P300, motor imagery, movement-related potential, steady-state visual evoked potential (SSVEP) and slow cortical potential have been utilized. Because of the superior signal-to-noise ratio (SNR) together with quicker information transfer rate (ITR), the intentness of SSVEP-based BCIs is progressing significantly. This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. The common-spatial pattern (CSP) has been employed to extract the features from SSVEP responses and these features have been classified through the support vector machine (SVM). The proposed architecture has achieved the highest classification accuracy of 88.3%. The experimental result proves that the proposed architecture could be utilized for the detection of SSVEP responses to develop any BCI applications. Keywords: EEG, BCI, SSVEP, CSP, SVM, Machine Learnin