EEG and Brain-Computer Interfaces

Multivariate pattern analysis of MEG and EEG: a comparison of representational structure in time and space

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.

Multichannel Sleep Spindle Detection Using Sparse Low-Rank Optimization

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.

Inter-Subject Alignment of MEG Datasets in a Common Representational Space

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

Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection

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

EyeBallGUI: A Tool For Visual Inspection And Binary Marking of Multi-Channel Bio-Signals

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

Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification

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.

Single-channel in-Ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech

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

Consistency of EEG source localization and connectivity estimates

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.

Filter-banks and artificial intelligence in seizure detection using electroencephalograms

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.

Comparison of linear spatial filters for identifying oscillatory activity in multichannel data

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