Jelmer P. Borst

The paper investigates whether active and passive items in working memory differ in susceptibility to interference, finding no significant difference in their vulnerability.

This paper introduces a hierarchical semi-Markov model with mixed additive effects to account for subject-level heterogeneity and covariate effects in neurophysiological data analysis.

This study investigates the role of alpha oscillations in working memory, finding that sustained alpha activity reflects attentional prioritization rather than general memory maintenance.

The paper uses single-trial EEG to identify five sequential decision-making events and their timing, supported by psychophysical laws and neural activity patterns.

This paper introduces a method to detect trial-wise cognitive events in neural time-series, showing it can estimate event patterns, sensor contributions, and inter-event intervals from EEG/MEG data.

This paper trains study-independent mind-wandering classifiers from EEG using CNNs, finding limited generalizability but improved performance with single-trial ERP features.

The paper identifies cognitive stages in MEG data using a novel method and develops a whole-brain model to simulate cortical synchrony during a task, showing thalamic bursts drive state transitions.

The paper presents a spiking-neuron model of human working memory using calcium-mediated synaptic plasticity, showing it can perform tasks as accurately as humans.

This paper introduces M-CCA for aligning MEG datasets across subjects by analyzing sensor recordings directly, showing improved performance over traditional methods.