Research ProposalPDF Available

Abstract

Aim of the project: David against Goliath: Could Small Data from single-channel or low-density EEG compete in the Big Data contest with high density EEG on equal footing or could David and Goliath join forces? Yes, they can and FlexEEG will show how. FlexEEG proposes a new concept of dry single-channel EEG with enriched information extraction that will materialize into a sensor-embedded data-driven approach to the real-time localization of brain activity. Level of impact and excellence: While a laboratory setting and research-grade electroencephalogram (EEG) equipment will ensure a controlled environment and high-quality multiple-channel EEG recording, there are situations and populations for which this is not suitable. FlexEEG aims at validating a new concept of single-channel or low-density EEG system that while being portable and relying on dry-sensor technology, will produce recordings of comparable quality to a research-grade EEG system but will surpass the capabilities and scope of conventional lab-based EEG equipment: In short, a single more intelligent EEG sensor could defeat high-density EEG. Conventional EEG is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. Ease of use and quality of information extraction are much awaited in a new EEG concept that produces recording of comparable quality to a research-grade system but that puts EEG within the reach of everyone. FlexEEG will bring that. This project will exploit methods of inverse problems, data-driven non-linear and non-stationary signal analysis 1,2,3 combined with dry-sensor technology to develop a pioneering system that will enable a single, properly localized EEG channel, to provide research-grade information comparable to and surpassing the capabilities of high density-channel EEG. Through this, the range of applications of EEG signals will be expanded from clinical diagnosis and research to healthcare, to better understanding of cognitive processes, to learning and education, and to today hidden/unknown properties behind ordinary human activity and ailments (e.g. walking, sleeping, complex cognitive activity, chronic pain, insomnia). This will be made possible by the implementation of adaptive non-linear and non-stationary data analysis tools in combination with inverse modelling to solve the brain-mapping problem.
Application form
Project tittle: David versus Goliath: single-channel EEG unravels its power through adaptive
signal analysisFlexEEG
Project leader: Marta Molinas, Professor, marta.molinas@ntnu.no , Department of Engineering
Cybernetics, Faculty and Information and Electrical Engineering
Co-applicants:
Audrey Van der Meer, Professor, Dept. of Psychology, Developmental Neuroscience Laboratory, Faculty of
Social and Educational Sciences
Nils Kristian Skjærvold, Post. Doc, Dept. of Circulation and Medical Imaging, Faculty of Medicine
Lars Lundheim, Professor, Dept. of Electronic Systems, Faculty of Inform. Tech. and Electrical Engineering
Aim of the project:
David against Goliath: Could Small Data from single-channel or low-density EEG compete in the Big Data
contest with high density EEG on equal footing or could David and Goliath join forces? Yes, they can and
FlexEEG will show how.
FlexEEG proposes a new concept of dry single-channel EEG enriched information extraction that will
materialize into a sensor-embedded data-driven approach to the localization in real-time of the brain
activity that originated the observed measurement.
Level of impact and excellence:
While a laboratory setting and research-grade electroencephalogram (EEG) equipment will ensure a
controlled environment and high-quality multiple-channel EEG recording, there are situations and
populations for which this is not suitable. FlexEEG aims at validating a new concept of single-channel or low-
density EEG system that while being portable and relying on dry-sensor technology, will produce recordings of
comparable quality to a research-grade EEG system but will surpass the capabilities and scope of conventional
lab-based EEG equipment: In short, a single more intelligent EEG sensor could defeat high-density EEG.
Conventional EEG is challenged by high cost, immobility of equipment and the use of inconvenient
conductive gels. Ease of use and quality of information extraction are much awaited in a new EEG concept
that produces recording of comparable quality to a research-grade system but that puts EEG within the
reach of everyone. FlexEEG will bring that.
This project will exploit methods of inverse problems, data-driven non-linear and non-stationary signal
analysis1, 2, 3 combined with dry-sensor technology to develop a pioneering system that will enable a single,
properly localized EEG channel, to provide research-grade information comparable to and surpassing the
capabilities of high density-channel EEG. Through this, the range of applications of EEG signals will be
expanded from clinical diagnosis and research to healthcare, to better understanding of cognitive
processes, to learning and education, and to today hidden/unknown properties behind ordinary human
activity and ailments (e.g. walking, sleeping, complex cognitive activity, chronic pain, insomnia). FlexEEG
proposes a new concept of dry single-channel EEG enriched information extraction that will materialize into
a novel sensor-embedded real-time data-driven approach to the localization of brain activities that
originated the observed recordings. This will be made possible by the implementation of adaptive non-
linear and non-stationary data analysis tools in combination with inverse modelling to solve the brain-
1 Norden E. Huang et. al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.
Proceedings of the Royal Society: Mathematical, Physical and Engineering Sciences, 1998.
2 M. Bueno-Lopez, M. Molinas, G. Kulia, Understanding Instantaneous frequency detection: A discussion of Hilbert-Huang Transform versus
Wavelet Transform, in Proceedings of the International Work-Conference on Time Series Analysis, September 18-20, 2017, Granada, Spain
3 Atin Das, Pritha Das, A. B. Roy, Nonlinear Data Analysis of Experimental (EEG) Data and Comparison with Theoretical (ANN) Data,
Complexity, vol. 7, no. 3, Wiley Periodicals, 2002
mapping problem4. Recent results achieved by the applicant’s team5,6, 7 shows a successful brain mapping
exercise based on a new methodology co-developed by the applicant for accurate separation of intrinsic
brain modes8, 9,10.
Level of impact: The concept of FlexEEG will place EEG signal recording and interpretation within
everyone’s reach. Today, a validated concept of this does not exist.
Conventional EEG systems require extensive setup time and computation time, expensive equipment, and
expertise to utilize and therefore are often limited to clinical or laboratory settings. One consequence of
high-density EEG is that interpretation in real-time is not available today. Technological advancements in
dry sensor system have opened avenues of possibilities to develop wireless and portable EEG systems with
dry electrodes to reduce many of these barriers. A previous validity study demonstrated that data derived
from a single-channel, wireless system (NeuroSky ThinkGear, San Jose, California) is comparable to EEG
recorded from conventional lab-based equipment. These findings encourage application of the portable
EEG system for the study of brain function in real-time. However, many untackled issues (e.g. real-time,
quality of recordings) remain yet unexplored11.
Unlike existing approaches, FlexEEG combines the hardware and software challenges together to web a
unified embedded solution that will incorporate the data analysis tool into the sensor and will design the
best suited dry sensor for the signals to be analyzed. This will materialize into a real-time Brain Computer
Interface (BCI) FlexEEG.
Publication level:
The initial results obtained in the last 2 years by the applicant’s team indicate a potential for long-term
research with prominent findings in this field. The identified gap in research is clear in both the hardware
and software for this concept and they await validation. Realizing the objectives of this research will
produce unique and unprecedented results that will materialize in at least two top-level publications. One
for the hardware validation of FlexEEG and a second for the mathematical framework that will enable EEG
interpretation at the reach of everyone. We anticipate an open source BCI beta version of the FlexEEG
concept to become one of the most prominent results of this project.
External financing:
The concept presented in FlexEEG will qualify for the Ideas Programme of the European Research Council
calls and for the FET call, after production of first validation in FlexEEG. It presents both a new and
promising concept that moves beyond what is known today, combining the strengths of multidisciplinary
sciences (non-linear and non-stationary capable analysis tools) and cutting-edge engineering (portable dry
sensors) to produce a unified imbedded solution materialized in FlexEEG. We anticipate that during the first
two years and with the results obtained in this project, either a FET or ERC Synergy proposal will be
prepared and submitted. In addition, with the current and expanded international allies, government
funding will be sought in partners countries (FRIPRO, Colciencias, NSF).
Project plan:
This project will address and propose solutions to the two main aspects of the same problem: i). The
barriers coming from lack of homogeneity and validation in hardware for dry sensor-based EEG. Due to the
lack of homogeneity in hardware, dry electrode evaluation, and methodological procedures, the clinical
acceptance of EEG based on dry sensors has been limited. ii) The barriers coming from the limited
availability of proven tools for non-linear and non-stationary data analysis
4 M. Bueno-Lopez, E. Giraldo, M. Molinas, A new method for localizing activity in the brain based on Empirical Mode Decomposition and
entropy function, 7th International Brain Computer Interface Meeting, California, May 2018
5M. Bueno-Lopez, E. Giraldo, M. Molinas, A new method for localizing the Focal Origin of Epileptic Activity using EEG Brain Mapping based on
empirical Mode Decomposition and entropy function, Submitted to Journal of Human Brain Mapping, under review, January 2018
6 M. Bueno-Lopez, E. Giraldo, M. Molinas, Localizing the Focal Origin of Epileptic Activity using EEG Brain Mapping based on Empirical Mode
Decomposition, submitted to the 26th European Signal Processing Conference (EUSIPCO 2018)
7 M. Bueno-Lopez, E. Giraldo, M. Molinas, Analysis of Neural Activity from EEG Data based on EMD frequency bands, 24th IEEE International
Conference on Electronics, Circuits and Systems (ICECS), December 5-8, Batumi-Georgia, 2017.
8 O.B. Fosso, M. Molinas, Method for Mode Mixing Separation in Empirical Mode Decomposition, in Open Repository of Cornell University
Library, arXiv:1709.05547 [stat.ME], 2017
9 M. Bueno-Lopez, E. Giraldo, M. Molinas, O. B. Fosso, Mode mixing problem and its influence in the neural activity reconstruction, to be
submitted to IEEE Journal of Selected Topics in Signal Processing, 2018
10 M. V. Gasca, M. Bueno-López, M. Molinas, and O. B. Fosso, Time-Frequency analysis for nonlinear and non-stationary signals using HHT: A
mode mixing separation technique, In IEEE Latin America Transactions, in Press, January 2018
From these two main challenges, the two Objectives of FlexEEG emerge:
O1: Develop and validate an “in-house” wireless dry electrode EEG system by comparing its performance to
a conventional wet electrode EEG system. In this experiment, the signal output of both EEG systems will
be examined in a sample of healthy adults (PhD1). High-risk: unavailable and unproven technology.
O2: Produce a new mathematical and computational framework for non-stationary, non-linear data
analysis of brain activity based on single-channel EEG recordings (PhD2). The main asset of this new
framework will be its seamless ability to decompose in real-time the brain recordings into single physically
meaningful brain modes that can be accurately mapped and localized through inverse problem solving.
Medium to High risk: preliminary results produced by the team exists while validation is needed.
Underlaying hypothesis: single-channel EEG can provide same and more information than high-density EEG.
On a fundamental level, FlexEEG will produce a novel mathematical framework for real-time data-driven
multi-purpose EEG signal interpretation tool. On a practical level, FlexEEG will show that this framework
can facilitate the best use of a single-channel data and apply it to a wide variety of brain tasks ranging from
research to education, biofeedback to daily trivial human activity recordings, and put it within everyone’s
reach.
The project will also address big-data challenges, as the proposed single-channel system will leverage multi-
channel biological signals information (EEG, ECG) to capture the many factors that may affect the response
of other biological signals (ECG) and of single-channel at different sites to tackle potential interaction issues
in multi-channel EEG and other biological signals12. Validation of this aspect will be conducted through
comparison with Multivariate-EMD (Empirical Mode Decomposition)13.
Methodology: The methodology is structured, as shown below, around the two Objectives into two PhD
studies to develop signal analysis methods and design a NTNU brandsingle-channel dry sensor EEG
system. The research team will jointly approach the goal across the involved disciplines with strong
interrelation (as shown in the Table). The research team members and their international allies have been
collaborating in Brain Computer Interface, and Signal Analysis, as attested in “Documented Competence”.
Competence in FlexEEG
NTNU Department
Partner
Documented Competence in Prior Art
Complex physiological
regulation in critically ill
patients
Dept. of Circulation
and Medical Imaging
N. K.
Skjærvold
Skjaervold et al: Multivariate analyses and the bridging
of biology’s “Math-Gap”. Encyclopedia of Analytical
Chemistry, invited manuscript submitted.
EEG Science,
Neuropsychology
Dept. of Psychology,
Developmental
Neuroscience Lab.
A. van der
Meer
Van der Meer, A., Van der Weel, F. (2017), Only three
fingers write, but the whole brain works: A high-
density EEG study showing advantages of drawing over
typing for learning. Frontiers in Psychology, vol. 8, May
Non-linear non-stationary
data analysis, BCI
Dept. of Engineering
Cybernetics
M. Molinas
References 2,4,5,6,7,8,9,10, 12 in this proposal
developed in the last 2 years.
Digital Signal Processing
Dept. of Electronic
Systems
L. Lundheim
Lundheim, L. M. (2002) On Shannon and "Shannon's
Formula".Telektronikk. vol. 98 (1).
12 K. Knai, G. Kulia, M. Molinas, N. K. Skjaervold, Instantaneous frequencies of continuous blood pressure: A comparison of the power
spectrum, the continuous wavelet transform and the Hilbert-Huang transform, Journal on Advances in Data Science and Adaptive Analysis,
vol. 9, no. 4, 2017
13 N. Rehman and D. P. Mandic, Multivariate Empirical Mode Decomposition, Proceedings of the Royal Society A, vol. 466, no. 2117, 2010.
Research Team & Goals
Research grade single-channel EEG system with embedded non-linear/non-stationary data analysis tool
Research Team: Marta Molinas, Lars Lundheim, Nils Kristian Skjærvold, Audrey Van der Meer
Objective 1 Supervisors (O1):
Molinas, Lundheim, Skjærvold
O1: Develop and validate “in-house” wireless dry
electrode EEG recording system
PhD1 (Yearly progress) Methodology PhD2 (Yearly progress)
Y1: Design and manufacture of suitable EEG dry-sensor
Y2: Validation against NTNU NuLab high density EEG
Y3: Integration of real-time data analysis in sensor (BCI)
Y2: Real-time computation of brain modes for brain mapping
Y3: Validation and Integration of data analysis in sensor
Results: 2 PhD Thesis, FlexEEG open source software, 2 IEEE Journal papers
Molinas Marta Curriculum Vitae-January 2018
Curriculum Vitae 2018
Marta Molinas,
http://www.ntnu.edu/employees/marta.molinas
Professional experience
From August 2014: Professor, Department of Engineering Cybernetics, Norwegian University of Science and
Technology (Trondheim, Norway)
From Dec. 2016: Affiliated Scientist at the Center of Excellence NTNU AMOS - Centre for Autonomous Marine
Operations and Systems
Jan. 2008- July 2014: Professor, Department of Electric Power Engineering, Norwegian University of Science and
Technology (Trondheim, Norway)
July 2013- July 2014: Visiting Scientist at Columbia University, Earth Institute, (New York, USA)
2008-2009: JSPS Postdoctoral Fellow at AIST, (Tsukuba, Japan)
2005-2007: Postdoctoral research associate, Center for Renewable Energy (SFFE-NTNU), supported by
competitive fellowship from the Research Council of Norway, (Trondheim, Norway)
2004-2005: Postdoctoral research associate, Department of Electric Power Engineering (NTNU) supported
by a competitive NTNU scholarship (Trondheim, Norway)
2000-2001: Research associate, Research Laboratory for Nuclear Reactors, (Tokyo, Japan)
Education
1997-2000 Dr. of Eng., Tokyo Institute of Technology (Tokyo, Japan)
1998 6 months PhD exchange, Dep. of Information Engineering, University of Padova, (Italy)
1994-1997 Master of Engineering, University of the Ryukyus, Okinawa, Japan
1994 Osaka University of Foreign Studies, Japanese proficiency
Scientific Leadership
Supervision: Graduated 16 PhDs and 45 Masters. Currently supervising 4 Posts docs, 1 PhD, and 13 Masters.
Honorary and Scientific Appointments
- Expert Evaluator for the European Research Council ERC-2016-STG, ERC-2017-STG, PE7: Systems and Communication
Engineering
- Expert Evaluator for the European Commission Horizon 2020 LC5 and LC6, 2015-2017
- Expert Evaluator for the ERA-Net Smart Grid Plus 2015, 2016
- Expert Evaluator for the European Commission FP7 DG Energy since 2008 to end of program
- Member of the Jury for Flagship Projects for the Austrian Research Promotion Agency, FFG since 2010
- Expert Evaluator for the Ministry of Education and Science of Russia
- Expert Evaluator for the Spanish Ministry of Economy, Industry and Competitiveness (Ramon y Cajal)
- Expert Evaluator for the Italian National Agency for the Evaluation of University Systems and Research (ANVUR)
Selected Journal and Conference Publications relevant to FlexEEG (last 2 years):
Complete list of publications available at: https://scholar.google.com/citations?user=hzJ_rK0reicC&hl=en
[J-2017] K. Knai, G. Kulia, M. Molinas, N. K. Skjaervold, Instantaneous frequencies of continuous blood pressure: A comparison of the
power spectrum, the continuous wavelet transform and the Hilbert-Huang transform, Journal on Advances in Data Science
and Adaptive Analysis, vol. 9, no. 4, 2017
[C-2017] M. Bueno-Lopez, M. Molinas, G. Kulia, Understanding Instantaneous frequency detection: A discussion of Hilbert-Huang
Transform versus Wavelet Transform, in Proceedings of the International Work-Conference on Time Series Analysis,
September 18-20, 2017, Granada, Spain
[C-2018] M. Bueno-Lopez, E. Giraldo, M. Molinas, A new method for localizing activity in the brain based on Empirical Mode
Decomposition and entropy function, 7th International Brain Computer Interface Meeting, California, May 2018
[J-2018] M. Bueno-Lopez, E. Giraldo, M. Molinas, A new method for localizing the Focal Origin of Epileptic Activity using EEG Brain
Mapping based on empirical Mode Decomposition and entropy function, Submitted to Journal of Human Brain Mapping,
under review, January 2018
[C-2017] M. Bueno-Lopez, E. Giraldo, M. Molinas, Localizing the Focal Origin of Epileptic Activity using EEG Brain Mapping based on
Empirical Mode Decomposition, submitted to the 26th IEEE European Signal Processing Conference (EUSIPCO 2018)
[C-2017] M. Bueno-Lopez, E. Giraldo, M. Molinas, Analysis of Neural Activity from EEG Data based on EMD frequency bands, 24th
IEEE International Conference on Electronics, Circuits and Systems (ICECS), December 5-8, Batumi-Georgia, 2017.
[P-2017] O.B. Fosso, M. Molinas, Method for Mode Mixing Separation in Empirical Mode Decomposition, in Open Repository of
Cornell University Library, arXiv:1709.05547 [stat.ME], 2017
[J-2018] M. Bueno-Lopez, E. Giraldo, M. Molinas, O. B. Fosso, Mode mixing problem and its influence in the neural activity
reconstruction, to be submitted to IEEE Journal of Selected Topics in Signal Processing, 2018
[J-2018] M. V. Gasca, M. Bueno-López, M. Molinas, and O. B. Fosso, Time-Frequency analysis for nonlinear and non-stationary signals
using HHT: A mode mixing separation technique, In IEEE Latin America Transactions, in Press, January 2018
Curriculum vitae with record of accomplishment (for established researchers)
Date of completion: 22.01.2018
PERSONAL
INFORMATION
*Family name, First name: Van der Meer, Audrey
*Date of birth: 01.10.1966
*Sex: Female
*Nationality: Dutch
*Married and mother of five children born between 1990 and 2002.
URL for personal web site:
https://www.ntnu.no/ansatte/audrey.meer
DN article 2014-
45K hits
EDUCATION
1992 PhD in Experimental Psychology: Disputation date: 28.11.1992.
Department of Psychology, Edinburgh University, Scotland (UK)
1988 Master of Science (MSc), two specializations: Philosophy and Psychology,
Interfaculty of Human Movement Sciences, Free University of Amsterdam, NL
CURRENT AND PREVIOUS POSITIONS
1997-present Full Professor of Neuropsychology
Department of Psychology, NTNU, Norway
1995-1997 Senior Lecturer of Biological Psychology
Department of Psychology, NTNU, Norway
1993-1995 Lecturer of Perception
Department of Psychology, Edinburgh University, Scotland (UK)
FELLOWSHIPS AND AWARDS
2016 Microsoft Europe, Learning project
1992-1995 Postdoctoral fellowship Medical Research Council, UK
1989-1992 Doctoral fellowship Medical Research Council, UK
1988-1989 Scholarship, British Council, UK
SUPERVISION OF GRADUATE STUDENTS AND RESEARCH FELLOWS
1993-present 5 Postdocs/ 7 PhD/ > 50 Master Students
Primarily at NTNU, Norway
Most influential publications highly relevant for FlexEEG:
1. Van der Meer, A.L.H. & Van der Weel, F.R. (2017). Only three fingers write, but the whole brain works: A
high-density EEG study showing advantages of drawing over typing for learning. Frontiers in Psychology, 8,
706 (>6200 views). https://doi.org/10.3389/fpsyg.2017.00706
2. Agyei, S., Van der Weel, F.R. & Van der Meer, A.L.H. (2016a). Longitudinal study of preterm and full-term
infants: High-density EEG analyses of cortical activity in response to visual motion. Neuropsychologia, 84,
89-104. http://dx.doi.org/10.1016/j.neuropsychologia.2016.02.001
3. Agyei, S., Van der Weel, F.R. & Van der Meer, A.L.H. (2016b). Development of visual motion perception
for prospective control: Brain and behavioral studies in infants. Frontiers in Psychology, 7, 100.
https://doi.org/10.3389/fpsyg.2016.00100
4. Agyei, S., Holth, M., Van der Weel, F.R. & Van der Meer, A.L.H. (2015). Longitudinal study of perception
of structured optic flow and random visual motion in infants using high-density EEG. Developmental Science,
18(3), 436-451. 10.1111/desc.12221
5. Van der Meer, A.L.H., Svantesson, M. & Van der Weel, F.R. (2013). Longitudinal study of looming in
infants with high-density EEG. Developmental Neuroscience, 34, 488-501.
6. Van der Weel, F.R. & Van der Meer, A.L.H. (2009). Seeing it coming: Infants’ brain responses to looming
danger. Die Naturwissenschaften, 96, 1385-1391.
7. Van der Meer, A.L.H., Fallet, G., Van der Weel, F.R. (2008). Perception of structured optic flow and random
visual motion in infants and adults: A high-density EEG study. Experimental Brain Research, 186, 493-502.
Curriculum Vitae 2018
Selected academic and professional publications relevant to FlexEEG:
1. G. Kulia, M. Molinas, L. Lundheim, O. B.Fosso, (2017), Simple model for understanding harmonics
propagation in single-phase microgrids. Proceedings of the 2017 IEEE 6th International
Conference on Clean Electrical Power
2. G. Kulia, M. Molinas, L. Lundheim, (2016) Tool for detecting waveform distortions in inverter-based
Microgrids: a validation study, 2016 IEEE Global Humanitarian Technology Conference
3. G. Kulia, M. Molinas, L. Lundheim, B. Larsen, (2016), Towards a real-time measurement platform
for microgrids in isolated communities. Procedia Engineering. vol. 159.
4. Tesfamicael, Solomon Abedom; Barzideh, Faraz; Lundheim, Lars. (2015), Improved
Reconstruction in Compressive Sensing of Clustered Signals. IEEE AFRICON 2015.
5. A. Shahmansoori, L. Lundheim. (2013), Second Order Taylor Polyphase Reconstruction of Periodic-
Nonuniform Samples in Time-Interleaved ADCs. Journal of Signal Processing Systems. vol. 77 (3)
6. S. Tjora, L. Lundheim. (2012) Distortion Modeling and Compensation in Step Frequency Radars,
IEEE Trans. Aerospace and Electronic Sys. vol. 48.(1) pp. 360-374
7. Mahmood, Nurul Huda; Øien, Geir Egil; Lundheim, Lars; Salim, Umer. (2012), A Relative Rate
Utility based Distributed Power Allocation Algorithm for Cognitive Radio Networks. IEEE
International Symposium on Personal, Indoor, and Mobile Radio Communications workshops.
8. Gardasevic, V., Muller, Ralf R.R., Zaidel, B.M., Øien, G.E., Lundheim, L. (2011), On the spectral
efficiency of MMSE vector precoding. IEEE Wireless Communications and Networking Conference;
2011
Lars Lundheim, Norwegian, M.Sc. 1985, PhD 1992, Senior Member of IEEE, is
Professor of Signal Processing
at Department of Electronics Systems, Norwegian
University of Science and Technology. His research work spans several fields of
signal processing, such as speech coding, satellite communication, image
compression, power efficient VLSI implementation, software radio, digital filters,
radar signal processing, mobile communication and ADC calibration. He has
been manager for several research projects, such as CUBAN 2004-2008
involving seven faculty members and nine PhD students. Of particular interest
for innovation and industry is ten years as Research Scientist at SINTEF,
working with projects for telecom, space and military sectors. He spent a
sabbatical in 2012 at Nordic Semiconductor working with performance analysis
of digital modulation schemes. He has supervised numerous Master student
projects for electronics and communications industry. Lundheim has been
supervising ten PhD students (main supervisor for six). His latest focus has been
in the topic of
"Compressed Sensing in Signal Processing: Performance Analysis
and Applications"
Curriculum Vitae
Nils Kristian Skjærvold
Born January 23rd 1975
Address: Gamle Åsvei 21, 7020 Trondheim, Norway
Phone: +47 99 37 57 74
Email: nils.k.skjaervold@ntnu.no
Medical education and degrees
SSAI two-year Advanced Educational Programme in Cardiothoracic and
Vascular Anaesthesia and Intensive Care, course approval April 2016
Philosophical Doctorate (PhD) in Clinical Medicine November 29th 2012, Norwegian University of
Science and Technology, Trondheim
Authorised as a specialist in anaesthesiology by the Norwegian Medical Association, November 16th 2010
Authorised as medical practitioner (MD) by the Norwegian Registration Authority for Health Personnel,
March 3rd 2005, Id # 8768358
Candidatus medicinae (MD) June 13th 2003, Faculty of Medicine, Norwegian University of Science and
Technology, Trondheim
Current positions
Jan 15 PostDoc Researcher at Department of Circulation and Medical Imaging, NTNU.
May 12 → Senior Consultant at Dep of cardiothoracic anaesthesia and intensive care medicine,
St Olavs Hospital, Trondheim University Hospital.
Work experience
April 14 Dec 14 Associate Professor at Department of Circulation and Medical Imaging, NTNU.
Jan 11 April 12 Senior Consultant at Dep of anaesthesia and intensive care medicine, St Olavs
Hospital, Trondheim University Hospital.
Oct 08 Dec 10 Combined Junior registrar and research-fellow (“D-stilling”) St Olavs Hospital and
Department of circulation and medical imaging, NTNU.
March 05 Oct 08 Junior Registrar at Dep of anaesthesia and emergency medicine, St Olavs Hospital,
Trondheim University Hospital.
Aug 04 Feb 05 Residency with general practice in Meløy, Nordland
Aug 03 Aug 04 Residency with hospital practice at Dep of medicine and Dep of surgery, Narvik
Sykehus.
Medical and academic interests
Medical technology and innovation with a focus on novel biosening methods
Non-linear dynamics in biological systems (complexity, chaos, oscillations and synchronizations)
Circulatory failure in cardiothoracic and general intensive care
Selected Publications and relevant to FlexEEG
N K Skjaervold, K Knai, N Elvemo: Some oscillatory phenomena of blood glucose regulation: An
observatory study in pigs. PLOS One, in press.
K Knai, G Kulia, M Molinas, N K Skjaervold: Instantaneous frequencies of continuous blood pressure
signalsA comparison of the power spectrum, the continuous wavelet transform and the Hilbert-
Huang transform. Advances in Data Science and Adaptive Analysis 2017;9(4),1750009.
A W Carlsen, N K Skjaervold, N J Berg, Ø Karlsen, E Gunnarson, A Wahba: Swedish-Norwegian co-
operation in the treatment of three hypothermia victims: a case report. Scand J Trauma Resusc
Emerg Med 2017;25(1):73.
N K Skjaervold, K Tøndel, G Cedersund, H Brovold, H Rahmati, L Munck, H Martens: Multivariate
analyses and the bridging of biology’s “Math-Gap”. Encyclopedia of Analytical Chemistry, 2017;
1:23.
H Langeland, O Lyng, P Aadahl, N K Skjaervold: The coherence of macrocirculation, microcirculation,
and tissue metabolic response during nontraumatic hemorrhagic shock in swine. Physiol Rep
2017;5:e13216.
K Knai, N K Skjaervold: A pig model of acute right ventricular afterload increase by hypoxic pulmonary
vasoconstriction. BMC Research Notes 2017;10:2.
... Although the use of resting-states as a biometric marker has been reported by several researchers [7,11,15], the possibility of using fewer channels or fewer instances has not been explored so far. As mentioned earlier in the paper, the use of 64 channels does not support the concept of a flexible, low-cost portable EEG-device as presented in [16]. The biometric systems currently adopted by the industry/market use about 5 instances or even fewer to add a new person (e.g., fingerprint, voice/face recognition, retinal scans) and in the research on biometric systems based on EEG, 192 or 55 instances per Subject, which is not practical for a real implementation. ...
... In [16] a new EEG concept of portable (non-invasive) dry single-channel or low-density EEG system, was introduced. While being portable and relying on dry-sensor technology, it will be expected to produce recordings of comparable quality to a research-grade EEG system but with wider scope and capabilities than conventional lab-based EEG equipment. ...
... The methods were applied to a dataset of resting-states from the low-cost EMOTIV EPOC device using 14 channels, 8 (P7, P8, O1, O2, F7, F8, T7 and T8), 4 (F7, F8, T7 and T8), 2 (T7 and T8) and 1 channel (T7) that were placed according to the 10-20 international system [20]. Subsets of channels were selected using a greedy algorithm [21] as a first attempt to move towards the FlexEEG Concept [16]. This is done in order to analyze the evolution of the accuracy using each time fewer channels, as it is explained later. ...
Chapter
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A new concept of low-density electroencephalograms-based (EEG) Subject identification is proposed in this paper. To that aim, EEG recordings of resting-states were analyzed with 3 different classifiers (SVM, k-NN, and naive Bayes) using Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) for feature extraction and their accuracies were estimated to compare their performances. To explore the feasibility of using fewer channels with minimum loss of accuracy, the methods were applied to a dataset of 27 Subjects (From 5 sessions of 30 instances per Subject) recorded using the EMOTIV EPOC device with 1 set of 14 channels and 4 subsets (8, 4, 2 and 1 channel) that were selected using a greedy algorithm. The experiments were reproduced using fewer instances each time to observe the evolution of the accuracy using both; fewer channels and fewer instances. The results of this experiments suggest that EMD compared with DWT is a more robust technique for feature extraction from brain signals to identify Subjects during resting-states, particularly when the amount of information is reduced: e.g., using Linear SVM and 30 instances per Subject, the accuracies obtained using 14 channels were 0.91 and 0.95, with 8 channels were 0.87 and 0.89 with EMD and DWT repectively but were reversed in favor of EMD when the number of channels was reduced to 4 channels (0.76 and 0.74), 2 (0.64 and 0.56) and 1 channel (0.46 and 0.31). The general observed trend is that, Linear SVM exhibits higher accuracy rates using high-density EEG (0.91 with 14 channels) while Gaussian naive Bayes exhibits better accuracies when using low-density EEG in comparison with the other classifiers (with EMD 0.88, 0.81, 0.76 and 0.61 respectively for 8, 4, 2 and 1 channel). The findings of these experiments reveal an important insight for continuing the exploration of low-density EEG for Subject identification.
... Although the use of resting-states as a biometric marker has been reported by several researchers [7,11,15], the possibility of using fewer channels or fewer instances has not been explored so far. As mentioned earlier in the paper, the use of 64 channels does not support the concept of a flexible, low-cost portable EEG-device as presented in [16]. The biometric systems currently adopted by the industry/market use about 5 instances or even fewer to add a new person (e.g., fingerprint, voice/face recognition, retinal scans) and in the research on biometric systems based on EEG, 192 or 55 instances per Subject, which is not practical for a real implementation. ...
... In [16] a new EEG concept of portable (non-invasive) dry single-channel or low-density EEG system, was introduced. While being portable and relying on dry-sensor technology, it will be expected to produce recordings of comparable quality to a research-grade EEG system but with wider scope and capabilities than conventional lab-based EEG equipment. ...
... The methods were applied to a dataset of resting-states from the low-cost EMOTIV EPOC device using 14 channels, 8 (P7, P8, O1, O2, F7, F8, T7 and T8), 4 (F7, F8, T7 and T8), 2 (T7 and T8) and 1 channel (T7) that were placed according to the 10-20 international system [20]. Subsets of channels were selected using a greedy algorithm [21] as a first attempt to move towards the FlexEEG Concept [16]. This is done in order to analyze the evolution of the accuracy using each time fewer channels, as it is explained later. ...
Conference Paper
Full-text available
A new concept of low-density electroencephalograms-based (EEG) Subject identification is proposed in this paper. To that aim, EEG recordings of resting-states were analyzed with 3 different classifiers (SVM, k-NN, and naive Bayes) using Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) for feature extraction and their accuracies were estimated to compare their performances. To explore the feasibility of using fewer channels with minimum loss of accuracy , the methods were applied to a dataset of 27 Subjects (From 5 sessions of 30 instances per Subject) recorded using the EMOTIV EPOC device with 1 set of 14 channels and 4 subsets (8, 4, 2 and 1 channel) that were selected using a greedy algorithm. The experiments were reproduced using fewer instances each time to observe the evolution of the accuracy using both; fewer channels and fewer instances. The results of this experiments suggest that EMD compared with DWT is a more robust technique for feature extraction from brain signals to identify Subjects during resting-states, particularly when the amount of information is reduced: e.g., using Linear SVM and 30 instances per Subject, the accuracies obtained using 14 channels were 0.91 and 0.95, with 8 channels were 0.87 and 0.89 with EMD and DWT respectively but were reversed in favor of EMD when the number of channels was reduced to 4 channels (0.76 and 0.74), 2 (0.64 and 0.56) and 1 channel (0.46 and 0.31). The general observed trend is that, Linear SVM exhibits higher accuracy rates using high-density EEG (0.91 with 14 channels) while Gaussian naive Bayes exhibits better accuracies when using low-density EEG in comparison with the other classifiers (With EMD 0.88, 0.81, 0.76 and 0.61 respectively for 8, 4, 2 and 1 channel). The findings of these experiments reveal an important insight for continuing the exploration of low-density EEG for Subject identification.
... There are various areas of application for which the creation of new EEG headsets could be interesting but the idea of comparing the use of static versus movable EEG electrodes for a single headset for di erent applications needs further exploration, as discussed in [56][57][58]. Recently, a research project entitled ...
Thesis
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The aim of this thesis is to move one step forward towards the concept of electroencephalographic (EEG) systems that can achieve the same objectives as high-density EEG with a minimum required number of channels. This requires EEG signal analysis, computational intelligence, and optimization techniques that can systematically identify the minimum number of channels that fulfills the objectives currently achieved with high-density EEG systems. Achieving this goal will pave the way towards the hardware-software realization of user-centric, easy-to-use, readily affordable EEG systems for universal applications. Enabling portability while ensuring performance of comparable or higher quality than that of high-density EEG will expand the accessibility of EEG to non-traditional users and personal applications moving EEG out of the lab. The application horizon will be expanded from experimental research to clinical use, to the gaming industry, intelligence and security sectors, education and daily use by people for self-knowledge. The methods proposed in the thesis comprise the combination of feature extraction techniques and channel selection algorithms with optimization techniques that allow extracting the most essential information from a minimum set of required EEG channels that were tested in two cases-studies: Epileptic seizure classification, and EEG-based biometric systems. The Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) were used to decompose EEG signals into different frequency bands and then four features were computed for each sub-band, the Teager and Instantaneous energies and the Higuchi and Petrosian fractal dimensions. For the optimization stage, non-dominated sorting genetic algorithms (NSGA) were used for channel selection, using binary values to represent the channels in the chromosomes, $1$ if the channel is used in the classification and optimization process, and $0$ if not. Additional genes to represent important parameters for the classifiers were added using integer and decimal values. For Case-study 1, NSGA-III selected one or two channels from a set of 22 for epileptic seizure classification, obtaining an accuracy of up to 0.98 and 1.00, respectively, using EMD/DWT-based features. For Case-study 2, a task-independent, resting-state-based biometric system using Local Outlier Factor (LOF)- and DWT-based features showed a True Acceptance Rate (TAR) of up to 0.993±0.01 and a True Rejection Rate (TRR) of up to 0.941±0.002 using only three channels selected by NSGA-III from a set of 64. The results presented herein can be considered to be a first proof-of-concept, showing that it is possible to reduce the number of required EEG channels for classification tasks and opens the way to explore these methods on other neuroparadigms. This will lead to reduced real-time computational costs for EEG signal processing, removing task-irrelevant and redundant information, as well as reducing the preparation time for use of the EEG headsets. The results of such a reduction in the number of required EEG channels will make possible a low-power hardware design, expanding the range of EEG-based applications from clinical diagnosis and research to health-care, to non-medical applications that can improve our understanding of cognitive processes, learning and education and to the discovery of current hidden/unknown properties behind ordinary human activity and ailments.
... In a low-density device, the multi-objective channel selection approach will be possible to use to modify the channel's position or at least the active sensors in real time and thus increase the classification accuracy, according to the ideas discussed in [3], [34]. But in a high-density EEG device the channel selection is not possible for real-time applications, even using greedy algorithms. ...
Conference Paper
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Current problems related to high-level security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded with electroencephalograms (EEG). In this paper, EEG-based brain signals of 56 channels, from event-related potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedback-related responses of a P300-speller system. The feature extraction part was done with empirical mode decomposition (EMD) extracting 2 intrinsic mode functions (IMFs) per channel, that were selected based on the Minkowski distance. After that, 4 features are computed per IMF; 2 energy features (instantaneous and teager energy) and 2 fractal features (Higuchi and Petrosian fractal dimension). Support vector machine (SVM) was used for the classification stage with an accuracy index computed using 10-folds cross-validation for evaluating the classifier's performance. Since high-density EEG information was available, the well-known backward-elimination and forward-addition greedy algorithms were used to reduce or increase the number of channels, step by step. Using the proposed method for subject identification from a positive or negative feedback-related response and then identify the subject will add a layer to improve the security system. The results obtained show that subject identification is feasible even using a low number of channels: E.g., 0.89 of accuracy using 5 channels with a mixed population and 0.93 with a male-only population.
... Essentially, this is because both, electrode localization and the number of electrodes, are task-dependent [1], [4], [8]. This opens the possibilities to explore the concept of "movable electrodes and a variable number of electrodes" [9]. ...
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This study reports the differences observed in the EEG signals of linguistic activity and resting-state between male and female subjects in a population of 16 individuals (8 females and 8 males). These differences were spotted while performing two experiments: sex identification and subject identification, where the initial aim was to identify the optimal number and placement of EEG channels to obtain high accuracies in sex and subject identification. The results of the identification show that the signals analyzed contain sex-specific information and that the best features from this sex-specific information are extracted from different EEG channel locations and from different hemispheres of the brain, for either sex. The effect of the number of electrodes and electrode localization is seen with clear differences between male and female subjects. The accuracy loss for sex identification when reducing the number of channels from 14 to 1 was of only 0.03 points during resting states (Accuracies from 0.79 to 0.76). For subject identification within either male or female groups during resting states, the accuracy loss was larger when reducing the number of channels from 14 to 1 (0.96 to 0.71 for female, 0.96 to 0.81 for male subjects). One finding of this study is that Theta and Gamma bands are strongest for males in the right hemisphere during resting states, whereas during linguistic activity these bands exhibit similar strengths in the left hemisphere for both males and females. Similar specific features in brain signals may enable the design of a flexible EEG device that can be adapted to specific mental tasks and Subject settings.
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This paper presents and discusses the challenge of mode mixing when using the Empirical Mode Decomposition (EMD) to identify intrinsic modes from EEG signals used for neural activity reconstruction. The standard version of the EMD poses some challenges when decomposing signals having intermittency and close spectral proximity in their bands. This is known as the Mode Mixing problem in EMD. Several approaches to solve the issue have been proposed in the literature, but no single technique seems to be universally effective in preserving independent modes after the EMD decomposition. This paper exposes the impact of mode mixing in the process of neural activity reconstruction and reports the results of a performance comparison between a well known strategy, the Ensemble EMD (EEMD), and a new strategy proposed by the authors for mitigating the mode mixing problem. The comparative evaluation shows a more accurate neural reconstruction when employing the strategy proposed by the authors, compared to the use of EEMD and its variants for neural activity reconstruction.
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Empirical Mode Decomposition (EMD) is an adaptive time-frequency analysis method, which is very useful for extracting information from noisy nonlinear or nonstationary data. The applications of this technique to Biomedical Signal analysis has increased and is now common to find publications that use EMD to identify behaviors in the brain or heart. In this work, a novel identification method of relevant IMFs, obtained from EEG signals, using an entropy analysis is proposed. The idea is to reduce the number of IMFs that are necessary for the reconstruction of neural activity. The entropy cost function is applied on the IMFs generated by the EMD. The efficacy of the proposed method has been demonstrated in a simulated and real data base. A relative error measure has been used to validate our proposal.
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