Christos-Savvas Bouganis

Inferring entire spiking activity from local field potentials with deep learning

This study uses deep learning to infer entire spiking activity from local field potentials in monkeys, showing higher accuracy than traditional methods.

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

This paper develops a deep learning method for decoding hand kinematics from neural signals, achieving high accuracy and robustness over long-term recordings in non-human primates.

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

The paper evaluates how different referencing schemes affect LFP-based BMI decoding performance and finds that CAR with LMP features improves long-term decoding.

Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

The paper introduces BAKS, a Bayesian method for estimating neuronal firing rates from single trials, showing improved performance over existing methods on synthetic and real data.