Theerawit Wilaiprasitporn

TL;DR: This review seeks to provide the detail of the products supplied by the major players, summarize studies that evaluate consumer product’s performance against research grade devices, the key areas of applications that these consumer grade devices have been employed in over the past five years or so, and finally give perspectives on the limitations and what these innovative tools could offer going forward.

TL;DR: In this article, a cascade of DL using a combination of convolutional neural networks (CNNs) and RNNs was proposed to improve the performance of affective EEG-based person identification.

TL;DR: Huang et al. as discussed by the authors used a transfer learning scheme to classify lung cancer using chest X-ray images, which achieved 74.43±6.01% of mean accuracy, 74.96±9.85% of average specificity, and 74.68±15.33% of overall sensitivity.

TL;DR: The event-related potential encoder network (ERPENet) is proposed, a multi-task autoencoder-based model that can be applied to any ERP-related tasks and its capability to handle various kinds of ERP datasets and its robustness across multiple recording setups, enabling joint training across datasets is proposed.

TL;DR: This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.