José del R. Millán

This study shows that real-time feedback on error-related potentials during perceptual training enhances error detection ability, outperforming conventional feedback methods.

This paper introduces a post-processing framework to improve real-time sleep staging reliability in neuromodulation by combining smoothing techniques and control logic.

This paper introduces a stretchable, hair-compatible EEG system that maintains signal integrity over long-term use and validates its performance in brain-computer interface tasks.

The paper develops a machine learning-based EEG-TMS system to predict corticospinal tract activation in real-time using personalized brain activity patterns in healthy adults.

The paper shows that different EEG pre-processing pipelines can lead to conflicting conclusions about cortical excitation/inhibition ratios.

This study shows that error-related potentials (ErrPs) encode both the magnitude of errors and individual perceptual thresholds through amplitude and latency, with implications for cerebellar contributions to performance monitoring.

The paper uses EEG to study brain responses during breaks in embodiment in VR, finding error-related potentials linked to non-embodied conditions.

The paper investigates the difference between stopping motor imagery (MI) and resting states, finding that MI termination can be detected faster and more accurately using Beta band features.

This study investigates how confidence in perceptual decisions arises by isolating decisional and post-decisional contributions using multimodal imaging.

This study shows that closed-loop DBS in Parkinson’s Disease can alter motor performance during specific movement intervals, particularly by increasing submovements and delaying movement termination.

This paper detects driver's movement intention in response to traffic lights using EEG-based decoding, showing anticipatory slow cortical potentials and single-trial classification performance.

This study shows that removing neural noise improves the link between beta oscillations in the STN and Parkinson's motor symptoms.

This paper conducts a meta-analysis of BCI-based post-stroke motor rehabilitation, finding a medium to large effect size on motor improvement.