3 papers
cs.LG2026
Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets
Laurits Dixen, Stefan Heinrich, Paolo Burelli
Classification of EEG signals using shallow Convolutional Neural Networks (CNNs) is a prevalent and successful approach across a variety of fields. Most of these models use indepen…
cs.LG2025
Exploring Deep Learning Models for EEG Neural Decoding
Laurits Dixen, Stefan Heinrich, Paolo Burelli
Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model…
cs.AI2025
Don't Get Too Excited -- Eliciting Emotions in LLMs
Gino Franco Fazzi, Julie Skoven Hinge, Stefan Heinrich +1
This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogue…