most citedEEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks

14 citations · 14 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers

Fotios Lygerakis, Ozan Özdenizci, Elmar Rückert

Tactile sensing provides local essential information that is complementary to visual perception, such as texture, compliance, and force. Despite recent advances in visuotactile rep…

cs.RO2025

ReLI: Cross-Lingual Language-to-Action Grounding for Human-Robot Interaction

Linus Nwankwo, Bjoern Ellensohn, Ozan Özdenizci +1

Adapting autonomous agents for real-world industrial, domestic, and other daily tasks is currently gaining momentum. However, in global or cross-lingual application contexts, the a…

cs.LG2021

Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks

Ozan Ozdenizci, Deniz Erdogmus

Feature ranking and selection is a widely used approach in various applications of supervised dimensionality reduction in discriminative machine learning. Nevertheless there exists…

eess.SP202114 cited

EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks

Ozan Ozdenizci, Safaa Eldeeb, Andac Demir +2

During daily activities, humans use their hands to grasp surrounding objects and perceive sensory information which are also employed for perceptual and motor goals. Multiple corti…

eess.SP2021

On the use of generative deep neural networks to synthesize artificial multichannel EEG signals

Ozan Ozdenizci, Deniz Erdogmus

Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering. In this paper we firstly review recently emerging studies on genera…