I am currently with Cognitive
Systems Laboratory (CSL).
In CSL, we are always in need of smart undergraduate and graduate
students. If you are interested in biomedical signal processing,
you can contact me with your CV.
Ph.D. in Electrical and Computer Engineering
Northeastern University, Boston, MA
Advisor: Deniz Erdogmus
B.Sc. in Electrical and
Bilkent University, Ankara, Turkey
High School: Izmir Scientific High School (Izmir Fen Lisesi)
 Murat Akcakaya, Betts Peters, Mohammad Moghadamfalahi, Aimee Mooney, Umut Orhan, Barry Oken, Deniz Erdogmus, Melanie Fried-Oken. Noninvasive Brain Computer Interfaces for Augmentative and Alternative Communication Biomedical Engineering, IEEE Reviews in , vol.PP, no.99, pp.1,1 (2013)
 Umut Orhan, Deniz Erdogmus, Brian Roark, Barry Oken, Melanie Fried-Oken. Offline Analysis of Context Contribution to ERP-based Typing BCI Performance J Neural Eng. 2013 Oct 8;10(6):066003
 Hooman Nezamfar, Umut Orhan, Shalini Purwar, Kenneth
E. Hild II, Barry Oken, Deniz Erdogmus. Decoding
of multichannel EEG activity from the visual cortex in
response to pseudorandom binary sequences of visual stimuli.
International Journal of Imaging Systems and Technology, vol.
21, pp. 139-147. (2011)
 M. Higger, M. Akcakaya, U. Orhan, D. Erdogmus. "Robust Classification in RSVP Keyboard". In Foundations of Augmented Cognition (pp. 443-449). Springer Berlin Heidelberg (2013).
 U. Orhan, D. Erdogmus, B. Roark, B. Oken,
S. Purwar, K.E. Hild, A. Fowler, and M. Fried-Oken.
"Improved Accuracy Using Recursive Bayesian Estimation Based
Language Model Fusion in ERP-Based BCI Typing Systems" In
Engineering in Medicine and Biology Society, EMBC, 2012
Annual International Conference of the IEEE, 2012
 Umut Orhan, Ang Li, and Deniz Erdogmus.
"Online regularized discriminant analysis." In Machine
Learning for Signal Processing (MLSP), 2012 IEEE
International Workshop on, pp. 1-6. IEEE, 2012.
 Umut Orhan, Kenneth E. Hild II, Deniz
Erdogmus, Brian Roark, Barry Oken, Melanie Fried-Oken. 2012.
RSVP Keyboard: An EEG based typing interface. Accepted to
 Umut Orhan, Kenneth E. Hild II, Deniz
Erdogmus, Brian Roark, Barry Oken, Melanie Fried-Oken. 2011.
Context Information Significantly Improves Brain Computer
Interface Performance - a Case Study on Text Entry Using a
Language Model Assisted BCI. Accepted to Asilomar, 2011.
 Umut Orhan, Deniz Erdogmus, Brian Roark, Shalini Purwar, Kenneth E. Hild II, Barry Oken, Hooman Nezamfar, Melanie Fried-Oken. 2011. Fusion with Language Models Improves Spelling Accuracy for ERP-based Brain Computer Interface Spellers. In 33nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'11).
 Kenneth E. Hild II, Umut Orhan, Deniz Erdogmus, Brian Roark, Barry Oken, Shalini Purwar, Hooman Nezamfar, Melanie Fried-Oken. 2011. An ERP-based Brain-Computer Interface for text entry using Rapid Serial Visual Presentation and Language Modeling. In Proceedings of the ACL-HLT 2011 System Demonstrations, pp. 38-43.
 Hooman Nezamfar, Umut Orhan, Deniz Erdogmus, Kenneth
E. Hild II, Shalini Purwar, Barry Oken, Melanie Fried-Oken.
visually evoked potentials in eeg induced by multiple
pseudorandom binary sequences for brain computer interface
design. In IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 2011 ,
pp.2044-2047, 22-27 May 2011
with severe speech and physical impairments can benefit from a
direct brain computer interface for their communication needs.
This project aims to develop an AAC interface using noninvasive
EEG sensors to infer the user's intent regarding desired letters
and symbols during text generation. The designed RSVP Keyboard
system will utilize rapid serial visual presentation of letter
sequences coupled with probabilistic and adaptive open
vocabulary language models and EEG signal processing and
classification algorithms. The designed brain interface relies
on event related potentials including the P300 signal. The
project design tightly couples feedback from locked-in
consultants who will test the system design at regular intervals
and provide critical feedback in future design improvements.
Most existing BCI
research for assistive communication interfaces focus on the use
letters to generate written text. This requires the subjects to
be literate and comfortable with typing in general. As an
alternative approach, in this project, we seek to design a BCI
that enables the user to generate language using an iconic
language called iconCHAT developed by Dr. Rupal Patel.
Expected benefits include increased speed of communication as
each icon might represent a word or phrase so equal number of
item selection can construct a sentence instead of a word
compared to RSVP Keyboard. A potential drawback is the necessity
for the subject to become familiar with the icon set; this also
means a closed vocabulary language generation system which is
suitable for limited context interactions. An option to switch
to letters for open vocabulary text generation can be
incorporated to the final design for users that prefer to have
SSVEP-based BCI Design
Steady state visually evoked potentials (SSVEP) of the visual cortex provide an alternative means for enabling communication between a brain and a computer system. Being relatively easy to induce, and requiring almost no subject training and minimal calibration time, SSVEP signals are prime candidates for developing BCI controlled applications. We are exploring the use of SSVEP-inducing stimuli and associated signal processing and classification algorithms primarily in environmental control applications such as navigating a robot/wheelchair. The reliable and robust nature of these signals also make them ideal for undergraduate research activities in BCI technology and application development .