Eduardo Fernández

The paper models phosphenes elicited by intracortical stimulation in human V1, showing that electrode stimulation can produce varied shapes due to activation of multiple locations.

The paper identifies cell-type-specific spiking patterns during cortical ripples in macaque and human V1, showing conserved circuit mechanisms across species and conditions.

This paper develops a deep learning framework to control neural activity in the human visual cortex using a neuroprosthesis, achieving better performance than conventional methods.

This study evaluates the acute biological response to Utah Optrode Array implantation in macaque cortex, identifying design parameters that minimize tissue damage and improve biocompatibility.

This study uses Bayesian optimization to find optimal stimulation patterns for cortical neuroprosthetics, improving phosphene perception quality through patient feedback.

The paper investigates valence classification of emotions using EEG, ECG, and skin temperature signals, finding that EEG alone performs as well as multimodal approaches.

This paper proposes a model for real-time EEG-based emotion recognition with high accuracy using wavelet analysis, sliding window parameters, and classifiers like QDA and KNN.