1 code implementation • 1 May 2021 • Ozan Ozdenizci, Deniz Erdogmus
We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient.
no code implementations • 17 Feb 2021 • Ozan Ozdenizci, Safaa Eldeeb, Andac Demir, Deniz Erdogmus, Murat Akcakaya
Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing.
no code implementations • 16 Feb 2021 • Ozan Ozdenizci, Deniz Erdogmus
Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering.
no code implementations • 28 Sep 2020 • Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
no code implementations • 26 Aug 2020 • Mo Han, Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
no code implementations • 15 Apr 2020 • Mo Han, Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
no code implementations • 22 Jul 2019 • Ozan Ozdenizci, Barry Oken, Tab Memmott, Melanie Fried-Oken, Deniz Erdogmus
Across- and within-recording variabilities in electroencephalographic (EEG) activity is a major limitation in EEG-based brain-computer interfaces (BCIs).
no code implementations • 28 Mar 2019 • Ozan Ozdenizci, Deniz Erdogmus
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics.
no code implementations • 27 Mar 2019 • Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG.
no code implementations • 17 Dec 2018 • Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs).