Mohammad R. Rezaei

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.

This paper introduces an SVD-based adaptive filtering method to remove both fast and slow artifacts from local field potentials during DBS, outperforming existing techniques on synthetic and real data.

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

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