The selection of stimulus contents for neurofeedback has direct implications on the efficacy of neurofeedback therapy. In particular, a suitable selection of stimulus contents facilitates the achievement of sustainability during neurofeedback sessions, which has been considered challenging during clinical practice. To further elaborate this point, this research investigates the efficacy of different neurofeedback stimulus contents (audio, video, and games) for stress mitigation. The effectiveness of the contents was measured by statistically comparing quantitative electroencephalogram (QEEG) features, such as alpha power and alpha asymmetry before and after neurofeedback sessions. In addition, the topographic maps of activities were constructed for a visual description. In this study, 29 study participants were recruited, and the EEG data were recorded during multiple neurofeedback sessions. ANOVA and post hoc testing verified the statistical significance of the results of the various stimulus contents, whereas a t-test verified the significance of stress mitigation because of neurofeedback. The results indicate that games exhibit higher effectiveness than audio and video contents for stress mitigation. In addition, the topographic analysis demonstrates the efficacy of neurofeedback training for stress mitigation. In conclusion, the effects of neurofeedback therapy could be enhanced while selecting suitable stimulus contents for neurofeedback protocols.INDEX TERMS Neurofeedback stimulus contents, QEEG analysis, neurofeedback for stress mitigation.

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Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.