Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of $98.73\%$ is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.

Maculopathy is a collective group of diseases that damages the central region of a retina known as macula. The major two forms of maculopathy are macular edema (ME) and central serous chorioretinopathy (CSCR). Different researchers have worked on the identification of these macular disorders using optical coherence tomography (OCT) images. However, to the best of our knowledge, no research is reported until now that can automatically extract retinal information for the grading of ME and CSCR as per clinically significant ME (CSME), non-clinically significant ME (non-CSME), Type-I CSCR, and Type-II CSCR clinical standards from OCT images. Therefore, this paper presents a novel structure tensor graph searches (ST-GS)-based segmentation framework that combines a structure tensor and graph theory to automatically extract retinal and choroidal layers along with fluid pores followed by automated reconstruction of 3-D retinal surfaces. The ST-GS can extract retinal information even from highly degraded OCT scans. Furthermore, the proposed system automatically grades ME and CSCR pathologies as CSME and non-CSME and Type-I CSCR and Type-II CSCR, respectively. The proposed system extracts seven distinct features, and it is trained on 30 (10 healthy, 10 ME, and 10 CSCR) labeled OCT volumes containing 3840 brightness scans (B-scans). After that, 90 (30 healthy, 30 CSCR, and 30 ME) unlabeled OCT volumes containing 11520 B-scans were used for testing the proposed system, where it correctly classified 88 out of 90 cases with the sensitivity, specificity, and accuracy ratings of 96.77%, 100%, and 97.78%, respectively. Furthermore, the proposed system has achieved a mean dice coefficient of 0.875±0.0342 and 0.92±0.0258 for extracting cyst and serous fluids, respectively.