Brain decoding, aiming to identify the brain states using neural activity, is important for
cognitive neuroscience and neural engineering. However, existing machine learning …

Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-
individual variations in functional brain organization challenge accurate decoding performed …

Decoding brain states under different cognitive tasks from functional magnetic resonance
imaging (fMRI) data has attracted great attention in the neuroimaging filed. However, the …

Neural decoding aims to extract information from neurons' activities to reveal how the brain
functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio …

Decoding brain cognitive states from neuroimaging signals is an important topic in
neuroscience. In recent years, deep neural networks (DNNs) have been recruited for …

A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An
emerging approach is “brain decoding”, which consists of inferring a set of experimental …

Brain decoding a pivotal field in neuroscience aims to reconstruct stimuli from acquired brain
signals primarily utilizing functional magnetic resonance imaging (fMRI). Currently brain …

Brain neural activity decoding is an important branch of neuroscience research and a key
technology for the brain–computer interface (BCI). Researchers initially developed simple …

Research in many fields has shown that transfer learning (TL) is well-suited to improve the
performance of deep learning (DL) models in datasets with small numbers of samples. This …

Functional magnetic resonance imaging (fMRI) produces low number of samples in high
dimensional vector spaces which is hardly adequate for brain decoding tasks. In this study …