Alexei E. Ossadtchi

This paper introduces a bilinear state-space model to track cortical traveling waves in MEG/EEG data, showing improved accuracy over traditional methods in simulations and empirical recordings.

The paper introduces SIGNAL, a dataset of sentences with grammatical and semantic variations used for EEG experiments to study language processing.

This study compares MEG and EEG in capturing physiological patterns and artifacts, finding MEG provides more spatially focused signals while EEG is more susceptible to high-amplitude artifacts.

This study uses MEG to compare brain activity during intentional and spontaneous gaze dwells in a video game task, identifying distinct neural patterns associated with voluntary gaze control.

CD-PSIICOS improves functional connectivity estimation by reducing task-related power leakage artifacts in MEG data, outperforming the original PSIICOS method.

This paper develops a lightweight EEG-to-fMRI neural network to predict subcortical BOLD activity from scalp EEG signals, achieving performance above chance and exceeding prior single-structure results.

ReDisCA estimates spatial-temporal components in EEG/MEG data aligned with a target RDM, improving source localization without inverse modeling.

This study shows that short-term meditation training increases resting brain activity and sympathetic responses, but not during meditation itself.

The paper demonstrates the optimality of the PSIICOS projection for suppressing spatial leakage while retaining zero-phase functional coupling information in EEG and MEG data.

This paper decodes speech from minimally invasive EEG electrodes using a compact neural network, achieving 55-70% accuracy with few channels.

The paper introduces FPCM, a novel spike detection method for EEG/MEG data, showing robustness in low SNR conditions and reliable detection of interictal events.

The paper presents a first solid-state fluxgate magnetometer for magnetoencephalography (MEG) and validates its feasibility by detecting the human alpha rhythm.

This paper introduces a modified covariance beamformer for MEG inverse problems, showing improved performance with correlated sources compared to existing methods.

This paper investigates reducing the visual field in a P300 speller to improve performance by minimizing non-target stimulus contributions.

This study shows that short-latency visual neurofeedback of parietal alpha activity enhances training effectiveness, with longer delays reducing sustained changes in alpha rhythm.

The paper extends previous findings on neuronal population dynamics by showing that both consistent and heterogeneous responses contribute to rotational patterns observed with jPCA, and suggests users consider these contributions when interpreting results.

The paper develops a method to localize epileptic seizure origins using MEG data by modeling spike propagation as traveling waves.

This paper develops algorithms to reduce feedback delay in brain rhythm-contingent paradigms using EEG data from human subjects.

The paper evaluates Churchland's claim that rotational dynamics represent a fundamental neuronal processing mechanism, finding that such patterns can arise from simple temporal shifts in firing rates and arbitrary simulations, suggesting they may not reflect meaningful neurophysiological structures.

This study uses ERP to examine how working memory load affects neural activity during simultaneous interpreting, finding that N1 amplitude varies with load but not interpretation direction.

This paper proposes an algorithm to reduce neurofeedback latency in EEG by optimizing FIR filter weights and analytic signal reconstruction, enabling predictive feedback with maintained accuracy.

This paper reanalyzes Hafting et al.'s 2005 grid cell data, clarifying that only one grid axis is tilted between subpopulations and that navigation traces align with grid orientation.

The paper introduces PSIICOS, a method to detect dynamically coupled networks from MEG data by analyzing interacting topographies and their temporal profiles.