A microfluidic device to perfuse pancreatic islets while simultaneously characterizing their functionality through fluorescence imaging of the mitochondrial membrane potential and intracellular calcium ([Ca²⁺]_i) in addition to enzyme linked immunosorbent assay (ELISA) quantification of secreted insulin was developed and characterized. This multimodal characterization of islet function will facilitate rapid assessment of tissue quality immediately following isolation from donor pancreas and allow more informed transplantation decisions to be made which may improve transplantation outcomes. The microfluidic perfusion chamber allows flow rates of up to 1 mL min⁻¹, without any noticeable perturbation or shear of islets. This multimodal quantification was done on both mouse and human islets. The ability of this simple microfluidic device to detect subtle variations in islet responses in different functional assays performed in short time-periods demonstrates that the microfluidic perfusion chamber device can be used as a new gold standard to perform comprehensive islet analysis and obtain a more meaningful predictive value for islet functionality prior to transplantation into recipients, which is currently difficult to predict using a single functional assay.

The recognition of emotions is one of the most challenging issues in human–computer interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions because of their ease of acquisition, mobility, and convenience. Deep neural networks (DNN) have provided excellent results in emotion recognition studies. Most studies, however, use other methods to extract handcrafted features, such as Pearson correlation coefficient (PCC), Principal Component Analysis, Higuchi Fractal Dimension (HFD), etc., even though DNN is capable of generating meaningful features. Furthermore, most earlier studies largely ignored spatial information between the different channels, focusing mainly on time domain and frequency domain representations. This study utilizes a pre-trained 3D-CNN MobileNet model with transfer learning on the spatio-temporal representation of EEG signals to extract features for emotion recognition. In addition to fully connected layers, hybrid models were explored using other decision layers such as multilayer perceptron (MLP), k-nearest neighbor (KNN), extreme learning machine (ELM), XGBoost (XGB), random forest (RF), and support vector machine (SVM). Additionally, this study investigates the effects of post-processing or filtering output labels. Extensive experiments were conducted on the SJTU Emotion EEG Dataset (SEED) (three classes) and SEED-IV (four classes) datasets, and the results obtained were comparable to the state-of-the-art. Based on the conventional 3D-CNN with ELM classifier, SEED and SEED-IV datasets showed a maximum accuracy of 89.18% and 81.60%, respectively. Post-filtering improved the emotional classification performance in the hybrid 3D-CNN with ELM model for SEED and SEED-IV datasets to 90.85% and 83.71%, respectively. Accordingly, spatial-temporal features extracted from the EEG, along with ensemble classifiers, were found to be the most effective in recognizing emotions compared to state-of-the-art methods.