EEG and Brain-Computer Interfaces

The paper derives and corrects analytical formulas for scalp potentials in the four-sphere EEG head model and compares them with FEM simulations.

This paper introduces a complexity-entropy map method to analyze EEG data from patients on psychotropic drugs, showing it can detect subtle changes in clinical response to treatment.

This paper introduces a spatiotemporal source separation method to improve signal-to-noise ratio and reduce dimensionality in multichannel neural data without using sinusoidal filters.

The paper introduces a new method for unmixing EEG/MEG inverse solution signals to correct for false connectivity, showing it produces accurate human connectomes under broad conditions.

This paper compares linear and nonlinear EEG analysis methods to detect differences between normal and epileptic signals using DFA and PSI.

This paper introduces a neuroconnectivity-based paradigm to enhance the similarity between movement imagination and execution in MI-BCI, showing improved session-to-session consistency in four out of five participants.

This study compares multivariate pattern analysis of MEG and EEG data to assess their representational structures in time and space, finding convergent and unique insights into visual processing.

This paper introduces a multichannel sleep spindle detection method using sparse low-rank optimization, achieving F1 scores of 0.66 and 0.62 on two databases.

This paper introduces M-CCA for aligning MEG datasets across subjects by analyzing sensor recordings directly, showing improved performance over traditional methods.

This paper shows that multivariate EEG analysis can track feature-based attentional selection with high spatial and temporal precision, outperforming standard N2pc methods.

EyeBallGUI provides a flexible GUI for visual inspection and binary marking of multi-channel bio-signals to improve artefact rejection and data processing.

This paper evaluates three EMD algorithms for mental task classification using EEG signals, showing improved performance over existing methods in both binary and multi-class scenarios.

This study shows that single-channel in-Ear-EEG can predict auditory attention focus in concurrent listening tasks.

This study assesses the consistency of EEG source localization and connectivity estimates across different models and software, finding variability in effective connectivity but stability in source localization.

The paper develops a method for seizure detection using EEG signals with a filter bank and C4.5 decision trees, achieving 94% accuracy using 10 frequency-related features.

This paper compares linear spatial filters for identifying oscillatory activity in multichannel data, finding that they can be powerful but require careful parameter selection.