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Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch’s power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time. The main purpose of this work is to apply the multiple signal classification (MUSIC) model, a parametric-based frequency-spectrum-estimation technique to extract features from multichannel EEG signals for emotional state classification from the SEED dataset. The main challenge of using MUSIC in EEG feature extraction is to tune its parameters for getting the discriminative features from different classes, which is a significant contribution of this work. Another contribution is to show some flaws of this dataset for the first time that contributed to achieving high classification accuracy in previous research works. This work used MUSIC features to classify three emotional states and achieve 97% accuracy on average using an artificial neural network. The proposed MUSIC model optimizes a 95–96% run time compared with the conventional classical non-parametric technique (Welch’s PSD) for feature extraction.

The trends in movement-related functional activity measurement for brain-computer interface (BCI) are mostly associated with the central lobe of the brain. This consideration may be a faulty approach for the paralyzed patient. This limitation demands an alternative approach for movement-related BCI. For the first time, we propose the prefrontal hemodynamics for implementing movement-related BCI. This paper aims to model the activation pattern and the classification performances of the prefrontal hemodynamics regarding the movement-related events. Utilizing functional near-infrared spectroscopy (fNIRS) the changes in the concentration of the oxidized hemoglobin and deoxidized hemoglobin regarding voluntary and imagery movements are acquired. With necessary preprocessing, the fNIRS signals are statistically analyzed to localize the most significant activated regions regarding the applied stimuli. The experiment shows that movement-related events have a strong correlation with the prefrontal hemodynamics. The patterns of the movement-related hemodynamics are modeled by polynomial regression and used to classify the voluntary and imagery events based on the maximum similarity approach. The resulting classification accuracies are found promising that proves the effectiveness of the prefrontal fNIRS signal to be effective in movement-related brain functionality analysis.

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

High temperature stability of band-gap energy of active layer material of a semiconductor device is one of the major challenges in the field of semiconductor optoelectronic device design. It is essential to ensure the stability in different band-gap energy dependent characteristics of the semiconductor material used to fabricate these devices either directly or indirectly. Different models have been widely used to analyze the band-gap energy dependent characteristics at different temperatures. The most commonly used methods to analyze the temperature dependence of band-gap energy of semiconductor materials are: Passler model, Bose–Einstein model and Varshni’s model. This paper is going to report the limitation of the Bose–Einstein model through a comparative analysis between Bose–Einstein model and Varshni’s model. The numerical analysis is carried out considering GaN as it is one of the most widely used semiconductor materials all over the world. From the numerical results it is ascertained that below the temperature of 95o K both the models show almost same characteristics. However beyond 95o K Varshni’s model shows weaker temperature dependence than that of Bose–Einstein model. Varshni’s model shows that the band-gap energy of GaN at 300o K is found to be 3.43eV, which establishes a good agreement with the theoretically calculated band-gap energy of GaN for operating at room temperature.