127 citations · 135 across the 8 of their papers we have counts for
8 papers
ADRMX: Additive Disentanglement of Domain Features with Remix Loss
Berker Demirel, Erchan Aptoula, Huseyin Ozkan
The common assumption that train and test sets follow similar distributions is often violated in deployment settings. Given multiple source domains, domain generalization aims to c…
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
Osman Berke Guney, Deniz Kucukahmetler, Huseyin Ozkan
Objective: SSVEP-based BCI spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transf…
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image
Berker Demirel, Huseyin Ozkan
Group Activity Recognition (GAR) aims to detect the activity performed by multiple actors in a scene. Prior works model the spatio-temporal features based on the RGB, optical flow…
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
Osman Berke Guney, Huseyin Ozkan
Objective: Steady-state visually evoked potentials (SSVEPs), measured with EEG (electroencephalogram), yield decent information transfer rates (ITR) in brain-computer interface (BC…
A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
Osman Berke Guney, Muhtasham Oblokulov, Huseyin Ozkan
Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the sub…
A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification
Basarbatu Can, Huseyin Ozkan
We propose a novel Neyman-Pearson (NP) classifier that is both online and nonlinear as the first time in the literature. The proposed classifier operates on a binary labeled data s…
Data Imputation through the Identification of Local Anomalies
Huseyin Ozkan, Ozgun S. Pelvan, Suleyman S. Kozat
We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder…
A Novel Training Algorithm for HMMs with Partial and Noisy Access to the States
Huseyin Ozkan, Arda Akman, Suleyman S. Kozat
This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we hav…