Ali Yousefi

The paper introduces a model to infer cognitive states during conflict processing in a Stroop task using neural and behavioral data from Parkinson's patients.

The paper evaluates an EEG-based focus detection system using Neurables Enten headphones and shows it accurately captures distractions across sessions and participants.

The paper proposes a computationally efficient approximate point process filter using mixture models for real-time decoding of neural spiking activity in multidimensional spaces.

The paper introduces time and rate rescaling transformations for assessing goodness-of-fit in marked-point process models of neural population coding using uniformity tests.

This paper systematically compares LSTM and state-space point-process models for neural decoding using rat place cell data, evaluating their performance, efficiency, and generalizability.

The paper introduces a new framework for analyzing neural synchrony across brain regions using state-space coherence, applied to study task-related neural activity in a Stroop task.

The paper introduces a direct decoder model for Bayesian state-space modeling to decode neural activity without an encoding step, achieving comparable performance to existing methods.

This study uses state-space modeling and intracranial stimulation to modulate hidden emotional states during conflict resolution, showing bidirectional effects on behavior.