Bakgrunn og aktiviteter

Project proposals for ITK 2020-2021 (Molinas)

1. FlexEEG: a new concept of low-density EEG for brain source localization

2. FlexEEG: EEG-based Alcohol Detector

3. Flying a Drone with your Mind: FlexEEG motor imagery

4. FlexEEG: Game based BCI system for ADHD Neurofeedback

5. FlexEEG: EEG-based Lie Detector

6. FlexEEG: Discrete Low-power and High Input Impedance EEG Amplifier

7. FlexEEG: Integrated Low-power and High Input Impedance EEG Amplifier

8. FlexEEG: Low-impedance 3D Printed Dry EEG Electrode

9. FlexEEG: EEG-based Biometric System for Subject Identification

10. The Augmented Human: Development of a color-exposure-based BCI with FlexEEG

11. FlexEEG in Sleep Research

                                                       Description of Projects

1. David and Goliath - FlexEEG: a new concept of dry single-channel EEG with brain mapping capabilities

This project is part of a larger project with 3 PhD students and one Post Doc under the NTNU Enabling Technologies Strategic Area. The project is in collaboration with the Department of Electronics Systems and the Developmental Neuroscience Laboratory of NTNU.

Traditionally EEG signals are recorded by several wet electrodes placed in a regular pattern on the scalp. To ensure stable and reliable readings a type of gel is usually applied to the scalp. The resulting procedure is both time consuming, expensive and uncomfortable for the patient.

As an alternative to the traditional method, single-channel EEG with single, dry, wireless, electrodes is proposed with FlexEEG. This simplified approach requires advanced electronics, signal processing, inverse problem solving and system identification competences. A team working towards such solutions is being built up, and Master students with background in electronics, signal processing, state estimation and embedded technologies are required.

 The research task will include: 

  • Development of an “in-house” EEG headset with flexible wireless dry electrode EEG system that can overcome the low SNR of dry sensors.
  • A main result of the study will be specifications for an ASIC (application specific integrated circuit) for optimized readout and digital transformation of the EEG sensor signal including wireless communication.

Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisors:

Lars Lundheim: NTNU IES,

Trond Ytterdal: NTNU IES,

Audrey Van Der Meer: NTNU Developmental Neuroscience Lab,

2. FlexEEG: EEG-based Alcohol Detector

The measurement of alcohol in the human body is a public health's relevant task for avoiding accidents and deaths. Currently, we can do that through devices that analyse the alcohol content in breath (Breathanalysers). However, it is not clear if a machine learning algorithm could help to get a better estimation of the effect of booze by directly using EEG signals. Previous works have only approached the automatic distinction (under a machine learning approach) between EEG signals from alcoholic vs non-alcoholic subjects with relatively good outcomes. 

This project aims to assess the feasibility of using EEG signals for detecting when a person has drunk alcohol, i.e, an initial design towards an EEG-based alcohol detector (Alcotest device).  The research in this project will imply the design of an EEG recording protocol from several subjects during and after the administration of alcohol and the main focus will be on assessing if a machine learning algorithm is able to effectively distinguish between the two states. Furthermore, it will involve the design of a scheme with the following stages: signal preprocessing, artifact removal, feature extraction, and classification.

Supervisors: Marta Molinas, marta.molinas@ntnu.no

Luis Alfredo Moctezuma, PhD candidate, luis.a.moctezuma@gmail.com    

3. Flying a Drone with your Mind: FlexEEG motor imagery

This project is about an experimentation on actuation of unmanned vehicles directly with signals from the brain. This will be done through the use of an open source Electroencephalography (EEG) headset that records brain activity and translates it into commands for actuation of devices in real-time. 

 

 

The task will consist on developing a Brain Computer Interface (BCI)  that can directly give flying/landing commands from the brain to the drone. The Open BCI EEG headset will be used (http://www.openbci.com/) for recording the brain signals to be processed into commands that will be sent wirelessly to actuate the drone. Motor imagery will be used as command to the drone. Using this command, this project will fly a drone and develop a trajectory control directly from the motor imagery commands without using any manual actuation system in between.

The students in this project will have access to the open-source softwares, EEG headset and BCI already developed within this task by previous year students. 

This project is suitable for two students to work in a team.

Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Luis Alfredo Moctezuma, PhD candidate, luis.a.moctezuma@ntnu.no   

4. FlexEEG: Game based BCI system for ADHD Neurofeedback

Applying machine learning, brain mapping and EEG recording techniques with an open source Brain-Computer-Interface (BCI) system, the master student will work in the development of a system for classifying the brain activity in imaginary motor tasks, with the objective of an user can navigate in a 3D virtual maze using his brain for treatment of Attention-Deficit Hyperactivity Disorder (ADHD).

Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Andres Soler, PhD candidate, andres.f.soler.guevara@ntnu.no   

5. FlexEEG: EEG-based Lie Detector

The aim of this project is to identify when a user is providing deceptive information using EEG signals. The master student will work in the development of a lie detection system, based on the analysis of EEG measuments, where he/she would combine knowledge of Event-Related Potentials, Signal Processing, Machine Learning and Brain Source Localization using an open source Brain-Computer-Interface (BCI) system, that will be provided for the project.

Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Andres Soler, PhD candidate, andres.f.soler.guevara@ntnu.no   

6. FlexEEG: Discrete Low-power and High Input Impedance EEG Amplifier

The aim of this proejct is to design a discrete biomedical amplifier, tailored for EEG signals. This amplifier should be compatible with the proposed dry electrode in the FlexEEG project (Project 8). It should have high input impedance (>10 GΩ), lower than 2 micro Vrms input referred noise in its bandwidth ( 0.5-100 Hz), low power (< 1 mW), high CMRR and PSRR (> 80 dB), high tolerable offset  ±300 mV, and 40-60 dB gain. Furthermore, it should be able to tolerate higher than ±300 mV offset. It is proposed to use chopping technique in order to reduce noise and offset while we are planning to integrate this circuit.

At the end, the performance of the amplifier will be checked with SAHARA electrode.

This project is suitable for two students to work in a team.

NTNU ITK Supervisor: Marta Molinas, marta.molinas@ntnu.no

NTNU IES Supervisor: Trond Ytterdal, 

Co-supervisor: Erwin Shad, PhD candidate, erwin.shad@ntnu.no   

7. FlexEEG: Integrated Low-power and High Input Impedance EEG Amplifier

In this project the student will work on an integrated amplifier for the same application as in Project 6. The final amplifier should have approximately the parameters listed below with lower power consumption, typically less than 10 micro watts.

1 GΩ   input impedance

Lower than 0,5 micro Vrms input referred noise in its bandwidth ( 0.5-100 Hz)

High CMRR and PSRR ( 120 dB)

High tolerable offset ±300 mV

Gain 40-60 dB

The student under this project will work in close collaboration with the students in project 6.

NTNU ITK Supervisor: Marta Molinas, marta.molinas@ntnu.no

NTNU IES Supervisor: Trond Ytterdal, 

Co-supervisor: Erwin Shad, PhD candidate, erwin.shad@ntnu.no   

8. FlexEEG: Low-impedance 3D Printed Dry EEG Electrode

The students in this project will design a customized EEG dry electrode with different models with a 3D printer. The printed electrode will be coated with conductive material and its impedance will be measured. Then the electrode to skin impedance will be measured. The work of the students will consist on designing the best architecturre of the electrode and on selecting the best coating conductor. 

After the headset is made with the 3D printer, the students will run experiments to compare their own produced electrodes to the best commercially available dry electrodes (SAHARA, Neuroelectrics, OpenBCI electrodes). At the end of the project, the concept designed in projects 6,7,and 8 will be compared to commercial ones by measuring EEG signals from subjects in a given neuroparagdigm.

This project is suitable for two students to work in a team and they will work in close collaboration with proejects 6 and 7.

Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Erwin Shad, PhD candidate, erwin.shad@ntnu.no   

9. FlexEEG: EEG-based Biometric System for Subject Identification

The aim of this project is to create a functional Subject Identification System using EEG signals. The system will consist of an interface with pyglet using a raspberry pi board and a touch screen with options to add and remove Subjects to the system. Feature extraction and machine learning techniques will be used to identify persons. The implementation will be tested in a real environment (opening a door if the Person has access) and/or displaying a tex in a screen such as “Welcome {Person_name}”.

Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Luis Alfredo Moctezuma, PhD candidate, luis.a.moctezuma@ntnu.no   

10. The Augmented Human: Development of a color-exposure-based BCI with FlexEEG

Allowing to control devices using EEG signals is one of the main applications of brain-computer interfaces. Nevertheless, two of the most common paradigms of control are based on an external flickering stimulator (SSVEP and P300). This project assesses the feasibility of using only the EEG responses to primary color exposure. This project has two scenarios (offline and online), which imply the application (or even the proposal) of algorithms for artifact removal, signal processing, machine learning, and their parameters' optimization. In the offline analysis, we will design a recording protocol and the main focus will be on assessing if a machine learning algorithm is able to effectively distinguish between colors. Whereas in the online scenario, the main task will be to develop the framework of the real-time control of some devices, for example, an automatized door.

This project is suitable for two students to work in a team.

The project is in collaboration with the Department of Neuroscience and Biomedical Engineering of Aalto University, Finland, with Dr Veikko Jousmäki.

NTNU ITK Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Andres Felipe Soler, andres.f.soler.guevara@ntnu.no 

11. FlexEEG in Sleep Research

The human brain is a large-scale network the function of  which depends on dynamic interactions between spatially distributed regions, in both normal and pathological states. This project is about understanding how the different regions connect in the brain during human sleep. For that purpose the student will perform a theoretical and experimental study of EEG source reconstruction and connectivity during human sleep by using first high density EEG recordings and from there to identify, using optimization and machine learning techniques, the minimal number of EEG channels  required for the same purpose. This study will use data from the Sleep Laboratory of the International Institute of Integrative Sleep Medicine of Tsukuba University, with which we are collaborating and the student could be part of.

NTNU ITK Supervisor: Marta Molinas, marta.molinas@ntnu.no

Co-supervisor: Andres Felipe Soler, andres.f.soler.guevara@ntnu.no 

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