Bettina Sorger

The paper examines how signal quality affects inter-individual variability in fNIRS data and introduces an open dataset with multi-task recordings and peripheral physiology measures.

This paper compares methods for detecting resting-state functional connectivity using fNIRS, finding that independent component analysis (ICA) outperforms seed-based approaches in accuracy and cross-chromophore consistency.

This paper quantifies how hair and skin characteristics affect fNIRS signal quality and provides recommendations to improve inclusivity in fNIRS studies.

This paper introduces a Riemannian geometry-based classification method for fNIRS data, achieving higher accuracy in brain-state classification compared to traditional methods.

This study assesses the feasibility of real-time interbrain synchrony measurement using fNIRS and wavelet transform coherence for neurofeedback applications.

The paper compares optode-layout design approaches for fNIRS using individual fMRI data, finding that incorporating more individual MRI data improves signal quality and sensitivity.

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

This study investigates whether pre-training brain activity can predict success in real-time fMRI neurofeedback learning across 24 studies and 401 participants, finding a weak correlation but no consistent predictors.

This paper demonstrates reliable single-trial decoding of somatosensory imagery using fMRI, showing high accuracy in distinguishing left-foot and right-hand imagery and developing a real-time BCI communication system.