Functional Connectivity Analysis in Multi-channel EEG for Emotion Detection with Phase Locking Value and 3D CNN.

The noise-assisted multivariate Empirical mode decomposition (NA-MEMD) is applied to multi-channel EEG signals to obtain narrow-band scale-aligned intrinsic mode functions (IMFs) upon which functional connectivity analysis is performed. The connectivity pattern in relation to inherent functional activity of brain is estimated with the phase locking value (PLV). Instantaneous phase difference among different EEG channels gives PLV that is used to build the functional connectivity map. The connectivity map yields spatial-temporal feature representation which is taken as input of the proposed emotion detection system. The spatial-temporal features can be learned with a 3D convolutional neural network for classifying emotion states. The proposed system is evaluated on two publicly available DEAP and SEED dataset for binary and multi-class emotion classification. On detecting low versus high level in the valence and arousal dimensions, the attained accuracy values are 97.37% and 96.26% respectively. Meanwhile, this system yields 94.78% and 99.54% accuracy on multi-class task on DEAP and SEED, which outperform previously reported systems with other deep learning models and conventional EEG features.

MEMD-HHT based Emotion Detection from EEG using 3D CNN.

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.

Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes.

In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification. MEMD is a data-adaptive analysis approach which is suitable particularly for multi-dimensional non-linear signals like EEG. Applying MEMD results in a set of oscillatory modes called intrinsic mode functions (IMFs). As the decomposition process is data-dependent, the IMFs vary in accordance with signal variation caused by functional brain activity. Among the extracted IMFs, it is found that those corresponding to high-oscillation modes are most useful for detecting different mental states. Non-linear features are computed from the IMFs that contribute most to mental state detection. These MEMD features show a significant performance gain over the conventional tempo-spectral features obtained by Fourier transform and Wavelet transform. The dominance of specific brain region is observed by analysing the MEMD features extracted from associated EEG channels. The frontal region is found to be most significant with a classification accuracy of 98.06%. This multi-dimensional decomposition approach upholds joint channel properties and produces most discriminative features for EEG based mental state detection.

EEG based stress analysis using rhythm specific spectral feature for video game play.

BACKGROUND AND OBJECTIVE: For the emerging significance of mental stress, various research directives have been established over time to understand better the causes of stress and how to deal with it. In recent years, the rise of video gameplay has been unprecedented, further triggered by the lockdown imposed due to the COVID-19 pandemic. Several researchers and organizations have contributed to the practical analysis of the impacts of such extended periods of gameplay, which lacks coordinated studies to underline the outcomes and reflect those in future game designing and public awareness about video gameplay. Investigations have mainly focused on the "gameplay stress" based on physical syndromes. Some studies have analyzed the effects of video gameplay with Electroencephalogram (EEG), Magnetic resonance imaging (MRI), etc., without concentrating on the relaxation procedure after video gameplay. METHODS: This paper presents an end-to-end stress analysis for video gaming stimuli using EEG. The power spectral density (PSD) of the Alpha and Beta bands is computed to calculate the Beta-to-Alpha ratio (BAR). The Alpha and Beta band power is computed, and the Beta-to-Alpha band power ratio (BAR) has been determined. In this article, BAR is used to denote mental stress. Subjects are chosen based on various factors such as gender, gameplay experience, age, and Body mass index (BMI). EEG is recorded using Scan SynAmps2 Express equipment. There are three types of video gameplay: strategic, puzzle, and combinational. Relaxation is accomplished in this study by using music of various pitches. Two types of regression analysis are done to mathematically model stress and relaxation curve. Brain topography is rendered to indicate the stressed and relaxed region of the brain. RESULTS: In the relaxed state, the subjects have BAR 0.701, which is considered the baseline value. Non-gamer subjects have an average BAR of 2.403 for 1 h of strategic video gameplay, whereas gamers have 2.218 BAR concurrently. After 12 minutes of listening to low-pitch music, gamers achieved 0.709 BAR, which is nearly the baseline value. In comparison to Quartic regression, the 4PL symmetrical sigmoid function performs regression analysis with fewer parameters and computational power. CONCLUSION: Non-gamers experience more stress than gamers, whereas strategic games stress the human brain more. During gameplay, the beta band in the frontal region is mostly activated. For relaxation, low pitch music is the most useful medium. Residual stress is evident in the frontal lobe when the subjects have listened to high pitch music. Quartic regression and 4PL symmetrical sigmoid function have been employed to find the model parameters of the relaxation curve. Among them, quartic regression performs better in terms of Akaike information criterion (AIC) and R2 measure.

Retracted Human Health Risk of Chromium Intake From Consumption of Poultry Meat and Eggs in Dhaka, Bangladesh.

BACKGROUND: Heavy metals contamination of food is a serious threat. Long term exposure may lead to human health risks. Poultry and eggs are a major source of protein, but if contaminated by heavy metals, have the potential to lead to detrimental effects on human health. OBJECTIVES: The objective of this study is to determine chromium concentrations in poultry meat (flesh and liver) and eggs collected from poultry farms in Dhaka, Bangladesh, to calculate the daily intake of chromium from the consumption of poultry meat and eggs for adults, and to evaluate their potential health risk by calculating the target hazard quotients (THQ). METHODS: All samples of poultry feed, meat (flesh and liver) and eggs were analyzed by a graphite furnace atomic absorption spectrometer (AAS) (GFA- EX- 7i Shimadju, Japan). RESULTS: Chromium concentrations were recorded in the range of not detected (ND) to 1.3926±0.0010 mg kg-1 and 0.0678±0.0001 mg kg-1 to 1.3764±0.0009 mg kg-1 in the liver of broiler and layer chickens, respectively. Chromium concentrations were determined in the range of 0.069±1.0004 mgkg-1 to 2.0746±0.0021 mg kg-1 and 0.0362±0.0002 mg kg-1 to 1.2752±0.0014 mg kg-1 in the flesh of broiler and layer chicken, respectively. The mean concentration of chromium in eggs was 0.2174-1.08 mg kg.-1 The highest concentration of chromium 2.4196±0.0019 mg kg-1 was found in egg yolk. Target hazard quotients values in all poultry flesh, liver and eggs samples were less than one, indicating no potential health risks to consumers. CONCLUSIONS: The estimated daily intake values of chromium were below the threshold limit. Thus, our results indicate that no adverse health effects are expected as a resultof ingestion of chicken fed with tannery waste. ETHICS APPROVAL: This study was approved by the Biosafety, Biosecurity & Ethical Committee of Jahangirnagar University.