Inter-subject and Intra-subject Variability: A Major Challenge in Brain-computer Interface

  • Wed 30 May 18

    16:00 - 18:00

  • Colchester Campus


  • Event speaker

    Dr Mahnaz Arvaneh

  • Event type

    Lectures, talks and seminars
    CSEE Seminar Series

  • Event organiser

    Computer Science and Electronic Engineering, School of

  • Contact details

Brain-computer Interface (BCI) is a rapidly growing area of research. Despite the impressive expansion in the recent years, none of the BCI systems described in the literature are sufficiently mature for the daily use out of the laboratory.


One of the major challenges in BCI is the nonstationary nature of EEG signals. The properties of EEG signals change considerably across sessions and subjects. This challenge has led to the need of calibrating a subject-specific BCI model for each subject at the beginning of each session. This calibration session which takes around 20-30 minutes is very time consuming, and tiring. In this talk, I will discuss a number of novel machine learning algorithms recently developed in our team to deal with the above mentioned challenge. The developed algorithms apply transfer learning techniques in raw EEG, features and classification domains to extract parts of information that are not only discriminative but also in common across different sessions and subjects. These algorithms will be evaluated using motor imagery-based BCI datasets and their advantages and disadvantages will be discussed in detail. Finally, future research directions in this filed will be discussed.

Inter-subject and Intra-subject Variability: A Major Challenge in Brain-computer Interface
Dr Arvaneh has joined the Department of Automatic Control and Systems Engineering (ACSE) at the University of Sheffield in September 2015 as a lecturer. She is also a member of the Sheffield Robotics Group which is one of the largest portfolios of ongoing publicly funded robotics research in the UK. Having received her PhD from Nanyang Technological University, Singapore (2013), she joined University College Dublin as a [fixed-term] lecturer and then she became a research fellow at Trinity College Institute of neuroscience, Ireland (2014-2015). Dr Arvaneh’s expertise is in adaptive signal processing and machine learning to accurately detect different biomarkers within brain and other physiological signals. She has incorporated these biomarkers in a range of robotic stroke rehabilitation, brain monitoring and cognitive performance enhancement experiments both in the laboratory and clinical settings. Dr Arvaneh currently serves as an Associate Editor of IEEE Transactions on Neural Systems and Rehabilitation Engineering. She is also a technical committee member of Asia-Pacific Signal and Information Processing Association (APSIPA), and IEEE Systems Man and Cybersecurity (SMC). 

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