Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

Time-frequency wavelet theory is used for the detection of life threatening electrocardiography (ECG) arrhythmias. This is achieved through the use of the raised cosine wavelet transform (RCWT). The RCWT is found to be useful in differentiating between ventricular fibrillation, ventricular tachycardia and atrial fibrillation. Ventricular fibrillation is characterised by continuous bands in the range of 2-10 Hz; ventricular tachycardia is characterised by two distinct bands: the first band in the range of 2-5 Hz and the second in the range of 6-8 Hz; and atrial fibrillation is determined by a low frequency band in the range of 0-5 Hz. A classification algorithm is developed to classify ECG records on the basis of the computation of three parameters defined in the time-frequency plane of the wavelet transform. Furthermore, the advantage of localising and separating ECG signals from high as well as intermediate frequencies is demonstrated. The above capabilities of the wavelet technique are supported by results obtained from ECG signals obtained from normal and abnormal subjects.

In this paper, we propose a novel technique for extracting fetal electrocardiogram (FECG) from a thoracic ECG recording and an abdominal ECG recording of a pregnant woman. The polynomial networks technique is used to nonlinearly map the thoracic ECG signal to the abdominal ECG signal. The FECG is then extracted by subtracting the mapped thoracic ECG from the abdominal ECG signal. Visual test results obtained from real ECG signals show that the proposed algorithm is capable of reliably extracting the FECG from two leads only. The visual quality of the FECG extracted by the proposed technique is found to meet or exceed that of published results using other techniques such as the independent component analysis.

Laser speckle imaging has increasingly become a viable technique for real-time medical imaging. However, the computational intricacies and the viewing experience involved limit its usefulness for real-time monitors such as those intended for neurosurgical applications. In this paper, we propose a new technique, tLASCA, which processes statistics primarily in the temporal direction using the laser speckle contrast analysis (LASCA) equation, proposed by Briers and Webster. This technique is thoroughly compared with the existing techniques for signal processing of laser speckle images, including, the spatial-based sLASCA and the temporal-based modified laser speckle imaging (mLSI) techniques. sLASCA is an improvement of the basic LASCA technique. In sLASCA, the derived contrasts are further averaged over a predetermined number of raw speckle images. mLSI, on the other hand, is the technique in which temporal statistics are processed using the equation described by Ohtsubo and Asakura. tLASCA preserves the original image resolution similar to mLSI. tLASCA outperforms sLASCA (window size M = 5) with faster convergence of K values (5.32 versus 20.56 s), shorter per-frame processing time (0.34 versus 2.51 s), and better subjective and objective quality evaluations of contrast images. tLASCA also outperforms mLSI with faster convergence of K values (5.32 s) compared to N values (10.44 s), shorter per-frame processing time (0.34 versus 0.91 s), smaller intensity fluctuations among frames (8%-10% versus 15%-35%), and better subjective and objective quality evaluations of contrast images. As laser speckle imaging becomes an important tool for real-time monitoring of blood flows and vascular perfusion, tLASCA is proven to be the technique of choice.

We present here one of the first studies that attempt to differentiate between genuine and acted emotional expressions, using EEG data. We present the first EEG dataset (available here) with recordings of subjects with genuine and fake emotional expressions. We build our experimental paradigm for classification of smiles; genuine smiles, fake/acted smiles and neutral expression. We propose multiple methods to extract intrinsic features from three EEG emotional expressions; genuine, neutral, and fake/acted smile. We extracted EEG features using three time-frequency analysis methods: discrete wavelet transforms (DWT), empirical mode decomposition (EMD), and incorporating DWT into EMD (DWT-EMD) at three frequency bands. We then evaluated the proposed methods using several classifiers including, k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). We carried out an experimental paradigm on 28-subjects underwent three types of emotional expressions, genuine, neutral and fake/acted. The results showed that incorporating DWT into EMD extracted more hidden features than sole DWT or sole EMD method. The power spectral feature extracted by DWT, EMD, and DWT-EMD showed different neural patterns across the three emotional expressions at all the frequency bands. We performed binary classification experiments and achieved acceptable accuracy reaching a maximum of 84% in all type of emotions, classifiers and bands using sole DWT or EMD. Meanwhile, a combination of DWT-EMD achieved the highest classification accuracy with ANN in classifying true emotional expressions from fake expressions in the alpha and beta bands with an average accuracy of 94.3% and 84.1%, respectively. Our results suggest combining DWT-EMD for future emotion studies and highlight the association of alpha and beta frequency bands with emotions.