School Of Electric, Electron & Comms Eng
Tel: +353 1 716
Mahnaz Arvaneh received the B.Sc. degree in electrical engineering from K.
N. Toosi University of Technology, Tehran, Iran, and the M.Sc. degree in
control engineering from Ferdowsi University of Mashhad, Iran, in 2005 and
2007, respectively. She pursued her PhD study at Singapore Nanyang
Technological University. She is currently a lecturer in Biomedical Engineering
in the School of
Electrical, Electronic and Communications Engineering, University College
Dublin. From April 2009 to
May 2013, she was an attached student in Institute for Infocomm research,
Agency for Science, Technology and Research, Singapore. In 2012, she was also a
visiting researcher in Electrical Engineering Department, National University
of Ireland, Maynooth.
Her current research interests include biomedical signal processing, brain¿computer interfaces, machine learning, pattern recognition and cognitive process with applications to neuro-rehabilitation and cognitive training.
Honours and Awards
| Year: 2009.
Title: Singapore International Pre-Graduate Award (SIPGA)
| Year: 2009.
Title: Singapore International Graduate Award (SINGA)
| Year 2007 Institution:
Qualification: MSc Subject:
| Year 2013 Institution: Nanyang Tech University, Singa
Qualification: PhD Subject:
|Ieee Transactions On Neural Systems And Rehabilitation Engineering: Reviewer.|
|Ieee Transactions On Neural Networks: Reviewer.|
|Neural Computation: Reviewer.|
|Computers In Biology And Medicine: Reviewer.|
|Biological Cybernetics: Reviewer.|
Peer Reviewed Journals
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, (2011) 'Optimizing the Channel Selection and Classification Accuracy in EEG-based BCI'. IEEE Transaction on Biomedical Engineering, 58 :1865-1873. [Details]|
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek (2013) 'Optimizing Spatial Filters by Minimizing Within-class Dissimilarities in EEG-based BCI'. IEEE Transactions on Neural Networks, 24 (4):610-619. [Details]|
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, (2013) 'EEG Data Space Adaptation to Reduce Intersession Non-stationarity in Brain-Computer Interface'. Neural Computation, 25 (8):2146-2171. [Details]|
|Z. S. Dastgheib, M. Khademi, A. Azemi, H. Gholizadeh, M. Shajiee, M. Arvaneh, V. R. Sabzevari (2009) 'Analysis and control of re-entry via ionic parameters identification in myocardium tissues' Journal of Technical Engineering . [Details]|
|Z.S. Dastgheib, A. Azemi, M. Khademi, M. Shajiee, M. Arvaneh, H. Gholizadeh, V.R. Sabzevari, (2009) 'Identification of Ionic Conductances in a Reentry Model of Ventricular Myocardium Cells' Journal of applied sciences . [Details]|
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, (2011) Spatially Sparced Common Spatial Pattern to Improve BCI Performance IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) [Details]|
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, (2012) Multi-frequency band common spatial pattern with sparse optimization in brain-computer interface IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan [Details]|
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek (2012) Robust EEG Channel Selection across Sessions in Brain-Computer Interface Involving Stroke Patients, Brisbane, Australia IEEE International Joint Conference on Neural Networks [Details]|
|M. Arvaneh, C. Guan, K. K. Ang, and C. Quek (2012) Omitting the Intra Session Calibration in EEG-based Brain Computer Interface Used for Stroke Rehabilitation 34th International Conf. of the IEEE Engineering in Medicine and Biology Society(EMBC),San Diego, USA [Details]|
My research in UCD focuses on developing machine learning and signal processing algorithms to reliably extract and analyse required information from bio-physiological signals with applications in brain-computer interface, rehabilitation, emotion recognition, sleep and anesthesia monitoring.
My research in Institute for Infocomm Research (I2R) included developing machine learning and signal processing algorithms to deal with noisy and non-stationary EEG signals in Brain-computer interface. The developed algorithms particularly aimed to make EEG-based Brain Computer Interface a more accurate and convenient technology for stroke rehabilitation.