A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically signifcant using z-test with p value <0.0001

Spontaneous Facial Expression Analysis Using Optical Flow Technique

© 2019, Springer Nature Switzerland AG. Investigation of emotions manifested through facial expressions has valuable applications in predictive behavioural studies. A potential application may be to impart intelligence to surveillance systems such as Closed-Circuit Television (CCTV) systems for recognition of emotional facial expressions. A facial recognition program tailored to evaluating facial behaviour for real time application can be met if patterns of emotions can be detected. An exploratory analysis of optical flow data was conducted with an aim to detect patterns and trends to differentiate between the emotional facial expressions: amusement, sadness and fear from the frontal and profile facial orientations. Analysis was in the form of emotion maps constructed from feature vectors obtained by using the Lucas-Kanade implementation of optical flow. Classification of individual emotions showed recognition of amusement was much greater in comparison to the recognition of the negative emotions, sadness and fear. Recognition was not negatively affected using reduced set of feature vectors derived from the emotion maps. Further investigation is necessary to assess the utility of emotion maps to visualise feature representations of emotional expression