5 citations · 6 across the 7 of their papers we have counts for
8 papers
EMG-GPT: Predictive Pretraining on Residual-Quantized EMG Tokens for Hand Pose Estimation
Ettore Magni, Rolandos Alexandros Potamias, Stefanos Zafeiriou +1
Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine whether self-supervised pretr…
Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
Urban Širca, Maryam Alimardani, Stefanos Zafeiriou +1
EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, demonstrating modest gains over supervised baselines and weak frozen represent…
EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models
Siegfried Ludwig, Stylianos Bakas, Konstantinos Barmpas +5
Deep learning for decoding EEG signals has gained traction, with many claims to state-of-the-art accuracy. However, despite the convincing benchmark performance, successful transla…
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
Konstantinos Barmpas, Na Lee, Dimitrios Chalatsis +7
Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, y…
Are Large Brainwave Foundation Models Capable Yet? Insights from Fine-tuning
Na Lee, Konstantinos Barmpas, Yannis Panagakis +3
Foundation Models have demonstrated significant success across various domains in Artificial Intelligence (AI), yet their capabilities for brainwave modeling remain unclear. In thi…
Advancing Brainwave Modeling with a Codebook-Based Foundation Model
Konstantinos Barmpas, Na Lee, Yannis Panagakis +3
Recent advances in large-scale pre-trained Electroencephalogram (EEG) models have shown great promise, driving progress in Brain-Computer Interfaces (BCIs) and healthcare applicati…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas +5
Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our app…