Aarhus Universitets segl

People

Head of Center

Preben Kidmose

Professor Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Researchers

Kaare Mikkelsen

Lektor Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Simon Lind Kappel

Adjunkt Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Anna Sergeeva

Postdoc Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Christian Bech Christensen

Akademisk medarbejder Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Phd Students

Heidi Bliddal Borges

Lønnet ph.d-stipendiat Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Jesper Strøm

Lønnet ph.d-stipendiat Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Bjarke Lundgaard Gårdbæk

Lønnet ph.d-stipendiat Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Jacopo Comoglio

Lønnet ph.d-stipendiat Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Nhan Duc Thanh Nguyen

Postdoc Institut for Datalogi

Research Assistents

Bowen Duan

Videnskabelig assistent Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Astrid Rands Bertelsen

Videnskabelig assistent Institut for Elektro- og Computerteknologi - Biomedical Engineering - Edison

Student Assistents

Andreas Nørtoft Jørgensen

Laura Rævsbæk Birch

Søren Baun Bech

Alumni

Annika Borgmeier

Master thesis: Impedance measurement of Electrodes for Ear-EEG. Spring 2023.

Katrine Græsbøll Christensen

Master thesis: Cognetive Prediction based on Sleep scoring. Autumn 2022.

Thomas Eriksen

Master thesis: Detection of seizure patterns in ear-EEG measurements using signal processing and machine learning. Autumn 2022.

Anisa Hassan

Master thesis: Investigation of binaural interaction effects in the auditory steady-state response. Autumn 2022.

Kajene Elankanathan

Master thesis: Investigation of binaural interaction effects in the auditory steady-state response. Autumn 2022.

Aksel Wilhelm Stark Madsen

Master thesis: Detection of sleep, speech, snoring and breathing with machine learning using body coupled microphone data. Autumn 2022.

Yousef Rezaei Tabar

Post Doc

Heidi Bliddal

Master thesis: Neural correlates of beat perception measured using ear-EEG. Spring 2021.

Rebina Francis Jeyapalan

Master Thesis: Headmodel on Ear-EEG. Fall 2020.

Anna Sergeeva

Master Thesis: Effect of stimuli bandwidth on the auditory steady-state response measured us-ing the ear-EEG method with focus on data processing optimization. Spring 2020.

Finja Jette Ralfs

Master Thesis: The effect of conductive gel on scalp potentials - A computational study. Fall 2019. 

Mette Hammer Nielsen-Kudsk

Master Thesis: A physical human head phantom with physiological properties for testing the effect of various conductive gels and amount, on scalp EEG potentials. Fall 2019. 

Camilla Arndal Andersen

Phd Thesis: Development of Methods for Objective EEG Analysis of Brain Activity induced by Sugar, Salt, Fat and their Substitutes

During the last decade, overweight and obesity have become an increasing global issue. According to WHO, in 2008, around 1.4 billion people over the age of 20 were overweight, at least 500 million were obese and at least 40 million children under the age of five were overweight.

The Food Industry's response to the obesity epidemic has been to produce a number of low fat and sugar food products that enable the consumer to eat the same food while consuming fewer calories. However, an investigation conducted by the Food Administration shows that people tend to consume extra-large servings of the light products, negating any benefits the light products might offer. 

A solution to the above-mentioned obesity epidemic requires a more thorough understanding of the brain's response to varying salt, sugar and fat levels and subjective satiation. Traditionally, food ingredient selection is based on physical and sensory analysis methods. However, in connection with salt, sugar and fat substitution products, objective measurement methods lack the ability to describe what we can register with our senses. In this regard, brain recordings are particularly interesting.

The idea behind the project is to utilise EEG methods to screen salt, sugar and fat substituents when selecting new food ingredients. The goal is to compare EEG results with physical or sensory data for new food ingredients with the hope of supplementing selection criteria for new food ingredients with objective physiological EEG responses.

Clara Rebbelstam

Development and Evaluation of an Experimental Para-digm and Analysis Method for Measuring Somatosensory Cortical Responses Evoked by Passive Movement in Amyotrophic Lateral Sclerosis. Fall 2018 

Camilla Pedersen

Development and Evaluation of an Experimental Para-digm and Analysis Method for Measuring Somatosensory Cortical Responses Evoked by Passive Movement in Amyotrophic Lateral Sclerosis. Fall 2018 

Henriette Bladt

Master Thesis: Generic EEG-earpiece. Fall 2018 

Astrid Rand Bertelsen

Master Thesis: Generic EEG-earpiece. Fall 2018.

    

Thomas Emil Møller Bartels

Master Thesis: Classification of Hand and Tongue Imagery Based on Mu-Rhythm Desynchronization Using EarEEG 

Peter S. Strange

Master Thesis: Ultra-low Power Wearable Miniaturized Pulse Oximeter 

Jaamac H. Hire

Master Thesis: Ultra-low Power Wearable Miniaturized Pulse Oximeter 

Karl-Johan Schmidt

Master Thesis: Multimodal-sensor solution for motion recognition and motion artifact reduction on wearable ear-EEG 

Simon Vammen Grønbæk

Master Thesis: Multimodal-sensor solution for motion recognition and motion artifact reduction on wearable ear-EEG 

Simon Lind Kappel

Phd Thesis: Characterisation and Evaluation of Dry-Contact Electrodes for Ear-EEG

 

Ear-EEG is a novel EEG (electroencephalography) recording approach in which the EEG signal is recorded from electrodes embedded on an ear-piece placed in the ear canal. The ear-EEG has great potentials within continuous brain monitoring in everyday life and will have application within both medical and consumer electronics devices.

The integration of brain monitoring based on EEG into everyday life has been hindered by the limited portability and long set-up time of current wearable systems as well as by the invasiveness of implanted systems.

To address these issues, the ear-EEG has been introduced which is a discreet, unobtrusive and user-centred approach to brain monitoring. The ear-EEG recording concept has been tested by using several standard EEG paradigms and benchmarked against standard on-scalp EEG.

All ear-EEG recordings made so far have been based on wet-electrode technology. In order to improve the usability and user-friendliness, this project will exploit so-called dry-contact electrode technology. This has impact on the design of the electrode itself, the supporting mechanics and the electronic instrumentation for acquiring the EEG signal.

Xiong Zhou

Phd Thesis: Dry-Contact Biopotential Acquisition Analog Front-Ends for Wearable Applications

David Bové Christensen

Master Thesis: Discreet and User-friendly Sleep Monitoring with Automatic Sleep Staging based on Ear-EEG 

Andreas Stochholm

Master Thesis: Automatic Sleep Stage Classification using Single Channel EEG and Ear-EEG