WebMay 13, 2024 · In this paper, we propose an adversarial deep domain adaptation approach for emotion recognition from electroencephalogram (EEG) signals. The method jointly learns a new representation that minimizes emotion recognition loss and maximizes subject confusion loss. WebDatasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challenges, we propose a self-supervised approach based on contrastive learning to model biosignals with a reduced reliance on labeled data and with fewer subjects.
Expression-Guided EEG Representation Learning for …
WebFeb 5, 2024 · convolutional neural network (CNN); electroencephalogram (EEG); topographic representation; brain–computer interface (BCI); EEG decoding; deep … WebObjective: The objective of this paper is to develop audio representations of electroencephalographic (EEG) multichannel signals, useful for medical practitioners and neuroscientists. The fundamental question explored in this paper is whether clinically valuable information contained in the EEG, not available from the conventional graphical … how many cm in a yard of fabric
EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG …
WebJan 26, 2024 · Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG electrodes, which results an … WebMar 27, 2024 · Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this paper, we propose a novel semi-supervised learning framework (EEGMatch) to leverage … WebOct 27, 2024 · We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning … how many cm in an a4 paper