Giancarlo Valente

This study investigates how theta-phase timing during encoding affects memory sensitivity using a closed-loop EEG system in an associative memory task.

The paper uses fMRI and RSA to show that food-related neural representations in occipitotemporal cortex reflect perceived calorie, health, and category structure, but not isolated calorie coding.

This study identifies classification inaccuracies in fMRI-based attention state decoding due to design choices and confounders like motion artifacts and task timing.

This paper improves sound recognition by incorporating semantic information into deep neural networks, showing better alignment with human perception than traditional categorical models.

The paper shows that out-of-sample R2 in fMRI encoding models is biased and develops a framework to correct it for unbiased model comparison.

This study evaluates two versions of NORDIC denoising on submillimeter auditory fMRI data, finding improved detection sensitivity and reliability but reduced average response amplitude.

This paper demonstrates reliable single-trial decoding of somatosensory imagery using fMRI, showing high accuracy in distinguishing left-foot and right-hand imagery and developing a real-time BCI communication system.

This paper transfers deep neural network features to fMRI data to improve decoding performance by reconstructing CNN features from imaging data.

This study evaluates methods for detecting phase effects in neural and behavioural responses using simulations and identifies optimal approaches for different response types.

This paper combines gamma, alpha, and beta power modulation from ECoG during cognitive tasks to improve identification of eloquent cortical areas in epilepsy patients, showing enhanced diagnostic performance over gamma alone.

This paper introduces a new analytical method for estimating the noise ceiling in fMRI data, comparing it to existing methods and showing its advantages for real data.