5 papers
Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
Urban Å irca, Maryam Alimardani, Stefanos Zafeiriou +1
EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, leaving their robustness, interpretability and representational quality largel…
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…
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…
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…
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…