Tutorials
The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.
Noninvasive Brain Computer Interfaces
Instructor
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Petia Georgieva
University of Aveiro
Portugal
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Brief Bio
Petia Georgieva (S’14) received the M.Sc. and PhD degrees in electrical engineering from Technical University of Sofia, Sofia, Bulgaria in 1989 and 2007, respectively. Since 2003 she has been a lecturer at the University of Aveiro, Portugal and researcher in the Institute of Electronics Engineering and Telematics of Aveiro (IEETA), Portugal. Dr. Georgieva was i) an invited professor in Rowan University, New Jersey in 2016; ii) a visiting faculty in Computer Science Department, Carnegie Mellon University (CMU), Pittsburgh, USA, 2012; iii) an invited researcher in Computer Science Department, University of Arkansas at Little Rock. USA, 2011 and iv) an invited researcher in the School of Computing and Communications, University of Lancaster, UK, 2011. Over the last ten years Dr. Georgieva works in the area of machine learning and data mining with strong focus on noninvasive Brain Computer Interfaces (BCI), brain neural activity recovering and affective neuro-computing based on EEG data. She has published more than 120 papers in peer reviewed journals and international conferences. Dr. Georgieva is a Senior member of IEEE, Senior Member of International Neural Network Society (INNS) and Elected Member of the Executive Committee of the European Neural Network Society (ENNS) for 2014-2016.
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Abstract
During the last decade advances in many scientific fields have supported the idea that a direct interface between the human brain and an artificial system, called Brain Computer Interface (BCI), is a viable concept, although a significant research and development effort has to be conducted before these technologies enter routine use. The principal reason for the BCI research is the potential benefits to those with severe motor disabilities, such as brainstem stroke, amyotrophic lateral sclerosis or severe cerebral palsy. However, recent advances in sensor technology and machine learning assert that using our brain for communication may have a significant impact in the way people will operate computers, wheelchairs, prostheses, robotic systems and other devices in the future. A promising way to analyze the brain physiological activity is the electroencephalogram (EEG) measurements from the cortex whose sources are the action potentials of the nerve cells in the brain. Over the last years, the interest in extracting knowledge hidden in the EEG signals is rapidly growing, as well as their applications. This tutorial will present the state of the art in the EEG-based Noninvasive BCI. The talk will highlight research on noninvasive BCI for motor control, reconstruction of brain active zones based on EEG and learning to decode human emotions with Echo State Networks.
Keywords
Brain computer interface,
Electroencephalogram (EEG),
Source-based BCI,
Bayesian estimation, Particle filters, Beamforming
Aims and Learning Objectives
Introduction to BCIs, biosignal processing and data modeling
Target Audience
Young researchers focused into biomedical signal processing, feature extraction, patterns recognition, statistical signal processing
Prerequisite Knowledge of Audience
Signal processing and filtering,
Linear algebra,
Kalman filters, Bayesian estimation theory
Detailed Outline
Electroencephalography (EEG)-based brain computer
interface (BCI) is the most studied noninvasive interface
to build a direct communication pathway between the brain and
an external device. However, correlated noises in EEG measurements
still constitute a significant challenge. Alternatively, building
BCIs based on filtered brain activity source signals instead of
using their surface projections, obtained from the noisy EEG signals,
is a promising and not well-explored direction. In this context,
finding the locations and waveforms of inner brain sources represents
a crucial task for advancing source-based noninvasive BCI
technologies. In this talk, a multicore beamformer
particle filter (multicore BPF) to estimate the EEG brain
source spatial locations and their corresponding waveforms is proposed. In
contrast to conventional (single-core) beamforming spatial filters,
the developed multicore BPF considers explicitly temporal correlation
among the estimated brain sources by suppressing activation
from regions with interfering coherent sources. The hybrid multicore
BPF brings together the advantages of both deterministic
and Bayesian inverse problem algorithms in order to improve the
estimation accuracy. It solves the brain activity localization problem
without prior information about approximate areas of source
locations. Moreover, the multicore BPF reduces the dimensionality
of the problem to half compared with the PF solution, thus alleviating
the curse of dimensionality problem. The results, based on
generated and real EEG data, show that the proposed framework
recovers correctly the dominant sources of brain activity.