17 citations · 23 across the 9 of their papers we have counts for
9 papers
Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG
Shiwen Chu, Shanglin Li, Motoaki Kawanabe +1
Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) often require unsupervised domain adaptation (UDA) to generalize across subjects and sessions. While Riemannian…
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
Bruno Aristimunha, Ce Ju, Antoine Collas +5
Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages. Existing impl…
An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon
Abhishek Jana, Moeumu Uili, James Atherton +3
This paper presents a largely automated one-shot bird call classification pipeline, incorporating targeted manual quality control steps, designed for rare species absent from large…
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
Shanglin Li, Motoaki Kawanabe, Reinmar J. Kobler
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neuro…
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
Ce Ju, Reinmar Josef Kobler, Cuntai Guan
The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input…
Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"
Patrick Ofner, Joana Pereira, Reinmar Kobler +2
Bibian et al. show in their recent paper (Bibián et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can gene…
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Reinmar J Kobler, Jun-ichiro Hirayama, Qibin Zhao +1
Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However,…
On the interpretation of linear Riemannian tangent space model parameters in M/EEG
Reinmar J. Kobler, Jun-Ichiro Hirayama, Lea Hehenberger Catarina Lopes-Dias +2
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer inter…
HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm
Reinmar J. Kobler, Andreea I. Sburlea, Valeria Mondini +1
A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact remova…