Seyed‐Mahdi Khaligh‐Razavi

The paper compares EEG, fMRI, and ECoG signals using visual object representations to understand their relationship in human neural activity.

The paper describes a multimodal neuroimaging dataset combining fMRI and EEG from 21 healthy volunteers to study spatiotemporal dynamics of visual processing.

The paper uses multivariate pattern analysis on EEG data to show that mild cognitive impairment patients process animacy information more slowly and with different temporal patterns compared to healthy controls.

The paper evaluates an AI-based cognitive assessment tool for detecting cognitive impairment in MCI and AD patients, showing good validity and generalisability across populations.

The paper develops and validates a 5-minute AI-based cognitive test (ICA) for MS patients, showing high reliability, validity, and accuracy in detecting cognitive impairment.

The paper corrects an error in noise ceiling calculations for human data from a previous study, showing the best model does not significantly fall below the noise ceiling lower bound.

The paper investigates object recognition under occlusion using MEG, behavioral data, and computational models, showing that recurrent processes are essential and that a local recurrent model explains human performance.

The paper develops a 5-minute computerized cognitive assessment tool (ICA) that reliably measures cognitive performance through visual processing speed and accuracy, showing strong correlation with standard tests.

The paper compares fixed and mixed representational similarity analysis (RSA) to explain visual representations in primate brain areas using shallow and deep computational models.

This paper characterizes the neural dynamics of visual memorability using MEG, finding that memorability is indexed by brain responses occurring later than early perception but earlier than memory encoding.