EEG electrode placement summary, April 2015

After experimenting with different EEG amplifiers, I got more information about different EEG electrode position. And then my summary ought to be revised. Below are the revisions in my PhD note.

• Motor imagery electrode placement (wp: 03.04.2015)
• Motor imagery electrode placement (blog: 03.04.2015)
• Motor imagery electrode placement, with figures (wp: 11.09.2013)
• Motor imagery electrode placement, with figures (blog: 11.09.2013)
• Mu wave electrode placement (wp: 06.04.2015)
• Mu wave electrode placement (blog: 06.04.2015)
• P300 electrode placement (wp: 04.04.2015)
• P300 electrode placement (blog: 04.04.2015)
• SSVEP electrode placement, with figures (wp: 06.12.2013)
• SSVEP electrode placement, with figures (blog: 06.12.2013)

In my notes, I add information about CSP spatial filter because this filter is suitable with EEG amplifiers with more than 8 electrodes. CSP is Common Spatial Filter. This filter is used for motor imagery BCI involving lateralization of hand movement: left versus right, contralateral versus ipsilateral.

***

Previous EEG electrode placement summary:

• EEG electrode placement summary (wp: 23.08.2014)
• EEG electrode placement summary (blog: 23.08.2014)

The EEG amplifiers, I have used so far in my PhD study at the University of Oldenburg, as well as in my master study at the University of Bremen, are below.

• mBrainTrain Smarting, a mobile EEG amplifier with 24 electrodes, since the second year of my PhD study.
• gtec MOBIlab+, a mobile EEG amplifier with maximum 8 bipolar electrodes, for the third semester of my PhD Study.
• Easymotiv, which is modified Emotiv EPOC with Easy Cap, a mobile EEG amplifier with 14 electrodes, for the first semester of my PhD study.
• TMSi Porti 7, an EEG amplifier with 32 electrodes, for my master thesis.
• gtec USBamp, an EEG amplifier with 16 electrodes, for my master project.

***

Tulisan ini adalah revisi dari rangkuman posisi elektroda EEG untuk Brain-Computer Interface sebelumnya. Revisi dilakukan setelah mencoba beberapa EEG amplifier. Dalam revisi terdapat penambahan informasi tentang Common Spatial Pattern (CSP) sesuai catatan doktoral 24 November 2014.


Bremen, 18 April 2015

iscab.saptocondro
Darah Juang!

Bandung Brain-Computer Interface on Indonesia Morning Show, Net TV, June 2014

Bandung Brain-Computer Interface (BCI) Bionic Arm (wp,blog) is on Indonesia Morning Show, Net TV. There we can see the demo:

• Putting the Emotiv EPOC headset.
• Calibration with ball and box from the laptop.
• Single-trial Motor imagery BCI  to control the robotic/bionic arm

From the video, I can see that BCI2000 is used to connect the Emotiv EPOC system and the bionic arm system. One student said that the price is less than 10 millions rupiahs. This means that Emotiv EPOC Research SDK (with a price of 750$) is used. Since August 2014, research SDK is not sold anymore and there is new Emotiv EPOC Education with a price of 1799$, now. It is unknown if the dll files still compatible with BCI2000 or not. Well, I had problem connecting Emotiv EPOC Education with OpenVibe.

The classifier are not explained nor seen from the video. It would be too complicated for "non-scientific" news channels. The calibration takes about 1 minute, on the video. The calibration method is using a ball and a box. On the monitor, there is a ball and a box. The ball should be moved closer to the box, using imagination of motoric movement. In calibration, both the machine (as classifier) and the human (as user) will learn to perform the motor imagery task. Well, 1 minute is fast. It is unknown whether the user had trained backstage before performing on camera. Haha!

For real-time usage of BCI, single-trial motor imagery has to be used. In calibration, more-trial motor imagery has to used for calculating the parameters of the classifier. From the video, we can see that the bionic arm moves only when the user do mental task of motor imagery. So I think the classifier is good and the user is "BCI-literate". When the user clapped her hand, the bionic arms didn't move. So I think the classifier detects hand grasping or finger movement and neglects other hand movement. In my opinion, the user has trained backstage before she is on camera. She has known that she has to imagine grasping movement. The classifier parameters may have been saved before the show. That's why she is doing calibration fast and she can easily move the bionic arm (without instruction from the experimenters).

From the video, there are 3 students (Electrical Engineering from ITB, Bandung, Indonesia). I think they have to separate tasks to do the projects. One should manage software and hardware to drive bionic arm. One should manage Emotiv EPOC connection with BCI2000. One should connect all the system to make sure that everything works. Also, one should design what kinds of classifier that works: linear discriminant analysis (LDA), common spatial pattern (CSP), or simple thresholding of ERP (event-related potential) or others. I think they use C++. From my experience with BCI2000, Microsoft Visual C++ has to be used. I haven't tried the new BCI2000. Maybe it works with C++ in many other environments: Eclipse and Linux.

Bandung Brain Computer Interface http://t.co/t4zLSuPI7l Indonesia Morning Show http://t.co/1ywMrmej1U
— iscab.saptocondro (@saptocondro) August 22, 2014

Well, I should go back to my real research, instead of blogging.


Bremen, 26 Agustus 2014

iscab.saptocondro
Darah Juang!

EEG electrode placement summary, August 2014

After reading the theses from my research mates from Uni Oldenburg, as well as Uni Bremen, I summarized some EEG electrode placements in my PhD note. So these are the links to my note.

• Motor imagery electrode placement (wp: 19.08.2014)
• Motor imagery electrode placement (blog: 19.08.2014)
• Motor imagery electrode placement, with figures (wp: 11.09.2013)
• Motor imagery electrode placement, with figures (blog: 11.09.2013)
• Mu wave electrode placement (wp: 19.08.2014)
• Mu wave electrode placement (blog: 19.08.2014)
• P300 electrode placement (wp: 22.08.2014)
• P300 electrode placement (blog: 22.08.2014)
• SSVEP electrode placement, with figures (wp: 06.11.2013)
• SSVEP electrode placement, with figures (blog: 06.11.2013)

SSVEP stands for steady-state visually evoked potential.

The idea is to minimize the number of EEG electrodes, which I am planning to use with my mobile EEG device. Putting electrodes to the scalp takes a lot of time. If 192 electrodes were put on whole scalp, it could took 1 hour or more and it would not be practical for the users. Also, analyzing the data from a large number of electrodes has a curse of dimensionality. I am planning to record using 8 or 16 or 24 EEG electrodes. Less is more! The EEG electrode placement which I need have been noted, so I just look into those specific electrode positions for my analysis, as well as, online signal processing.  

***

Tulisan ini adalah rangkuman dari posisi elektroda EEG untuk Brain-Computer Interface (BCI) berbasis SSVEP, P300,  dan "motor imagery" atau gelombang mu, untuk bulan ini.

Bremen, 23 Agustus 2014

iscab.saptocondro
Darah Juang!

Bandung Brain Computer Interface: Bionic Arm 2014

Previously, I have written about Bandung Brain Computer Interface (1,2,3) and the lack of research publication from Indonesia about BCI (wp, blog). Now, there are new youtube video from Ary Setijadi Prihatmanto, my former lecturer at the Electrical Engineering of Institut Teknologi Bandung (ITB). The video is about the research on bionic arm, controlled by Brain Computer Interface (BCI). The BCI Bionic Arm will be shown in the Electrical Engineering Day on June 2nd until 7th, 2014 at the Institut Teknologi Bandung (ITB).



From the video, I have found out that BCI2000 and Emotiv EPOC are used. Of course, MATLAB is also used. However, EEGLAB as a MATLAB toolbox for EEG analysis is not used. OpenVibe works only with Emotiv EPOC Research SDK. If other SDKs are used, for example Education SDK, then instead of OpenVibe, BCI2000 is the right framework to get EEG data from Emotiv.

Beside Electroencephalography (EEG), the video shows also Electromyography (EMG). Based on these signals, a bionic arm is controlled. The movement is hand opening and closing. From the video, there is an example of active motoric execution of a human participant. The subject wears Emotiv EPOC headset and move his hand actively. Emotiv EPOC sends the EEG signals via bluetooth to the computer and then a software decode these signals and transform them into commands for controlling the bionic arm.

http://t.co/XdxDlQZDEM Bandung BCI Bionic Arm. http://t.co/wD3yxbsXU9
— iscab.saptocondro (@saptocondro) May 28, 2014

I hope there will be other good news from Bandung BCI in the near future. Now, I am back to my real research, instead of blogging.


Bremen, 28 Mei 2014

iscab.saptocondro
Darah Juang!

How seriously is Indonesia doing research on Brain-Computer Interface?

My previous blog post is about Brain-Computer Interface (BCI) research in Bandung, Indonesia (1,2,3). The research is conducted in School of Electrical Engineering and Informatics (STEI) at Institut Teknologi Bandung (ITB). I know some people who has taken part in this Bandung BCI research. However, I wonder what and how are the research outcomes.

Below is the figure of literature study of EEG-based BCI between 2007 and 2011 from Hwang, et al in 2013. The paper is "EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey" in International Journal of Human-Computer Interaction, 29: 814-826 (doi), from Taylor & Francis. The authors are Han-Jeong Hwang, Soobeom Choi and Chang-Hwam Im from Hanyang University in Seoul, Korea, and Soyoung Kim from University of Rochester, New York, USA.

The figure above shows the nationalities of the authors of EEG-based BCI articles between 2007 and 2011. The articles have to be indexed by Web of Science, a database provided by Institute for Scientific Information (ISI, Thompson Scientific, Philadelphia, USA). Conference abstracts and editorials are not included. So the articles are research paper, review paper, feature, brief communication, case report, technical note and chronology.

As we can see from the figure, there are no Indonesian. So Bandung BCI group have never published their research in journals, indexed by ISI. As far as I know, the Bandung BCI group also had a collaboration with the Faculty of Medicine of Universitas Indonesia in Salemba, Jakarta, Indonesia. From some conference papers I have read, Institut Teknologi Telkom (IT Telkom) in Bandung are also doing research on EEG signals processing. But universities and also research institutes in Indonesia have not published scientific articles about EEG-based BCI in reputable journals between 2007 and 2011.

I get a feeling that Indonesian researchers and scientists publish their works more in conferences than journals. #fb
— iscab.saptocondro (@saptocondro) December 5, 2013

Well, I am Indonesian, currently doing research on EEG-based BCI. I just began my PhD program this September 2013 at Carl von Ossietzky Universität Oldenburg in Germany. So I have not yet published any journal papers. I hope, as an Indonesian, I play important role in international research on Brain-Computer Interface (BCI). In the next literature review, I hope there will be at least one Indonesian and that shall be me.

OK, now I should stop blogging and doing real research. :-)


Bremen, 6 Desember 2013

iscab.saptocondro
Darah Juang!

SSVEP electrode position, on my head

Since Posterous is killed by Twitter, pictures from my blog posts about standard 10-20 system are gone. The figures are not kept  by Posterous nor sent to Blogger. So I post the pictures again, which were saved by Wordpress:

• Standard 10-20 system and me, iscab
• Standard 10-20 system and me, saptocondro

I am wearing EEG cap

The pictures are about EEG electrode placement for SSVEP-BCI, according to 10-20 system (wiki: en,de). EEG is electroencephalography, which is recording of electrical signals from scalp (wiki: en,de,id). SSVEP is steady-state visually evoked potential, which is EEG signal as a response of flickering stimuli with certain frequency (wiki: en). BCI is Brain-Computer Interface (wiki: en,de).

10-20 system for EEG electrode position

The electrode position for SSVEP BCI  is Pz, PO3, PO4, O1, Oz, O2, O9 and O10. The ground is AFz and the reference is one ear lobe, either left or right. This position is used on my master thesis (book,slide).

Bremen, 6 Desember 2013

iscab.saptocondro

P.S. Now, I am thinking about EEG electrode position, for BCI, based on motor imagery.

Ethical Questions on Brain Computer Interface, by Paul Root Wolpe

Paul Root Wolpe had a talk "It's time to questions bio-engineering" on TED.

In the talk, there are applications of Brain Computer Interface (BCI):

• Monkey controls robotic arm with brain computer interface. The monkey sits in a room watching monitor and are put on EEG electrodes. In other room, there are robotic arm and camera. The monkey can see from the monitor what happens to robotic arm in the other room through camera. First, the monkey moves his own arm and robotic arm will make the same movement. After some repetitions, the monkey stops moving his arm and the robotic arm moves with the monkey's will.
• Flies and other insects are put on a chip to their "brain". Then engineers can control the insects to fly with a remote control. Infact, engineers and scientist have made breakthrough with mammals: rats. Here animals are losing the autonomy of their bodies. There are ethical questions on consciousness and autonomy.
• Eels' brain is put in gel which is put on electrodes. This bio-electronic system is put in a mobile robot as the main controller. The robot has a sensor and wheels. The robot can move toward the light although there are no programming codes to do so. A robot which is directly controlled by brain. (My notes: other researchers in different countries have successfully use rat brain tissues, see here)

Enjoy the video:

http://www.ted.com/talks/paul_root_wolpe_it_s_time_to_question_bio_engineering.html

 

Nürnberg, 26 Februari 2012

iscab.saptocondro

 

 

Bandung Brain Computer Interface

This is a short video of Brain Computer Interface research in Bandung, Indonesia.

The research is conducted in the School of Electrical Engineering and Informatics (STEI) in Institut Teknologi Bandung (ITB).

 

iscab.saptocondro

Standard 10-20 system EEG & me

My head

standard 10-20 system of electrode placement for EEG

Some scientific papers mentioning me and Brain-Computer Interface

I had involved in Brain-Computer Interface (BCI) research for 2 years. The research was done while studying in the University of Bremen. Now I have got M.Sc. degree in Information and Automation Engineering. After the end of my master study, there have been one master thesis, one master project report and 2 scientific papers related to BCI and me.

***

The master project report

Title: Final Preparation of the CeBit Data

author: Ignatius Sapto Condro Atmawan (That's me!)

supervisors: Prof. Dr.-Ing Axel Gräser & Dr.-Ing Ivan Volosyak

year: 2009

place: Institute für Automatissierungstechnik (IAT), Universität Bremen, Bremen, Germany

about:

The master project is mainly about EEG data format in the IAT. The BrainRobot group from the IAT conducted experiments about steady-state visual evoked potentials (SSVEP) in CeBit 2008, Hannover and RehaCare 2008, Düsseldorf, Germany. Both experiments used different data formats. The project report mentions other alternatives of data format which have already been international standards or at least european ones: GDF, EDF, BDF and BKR.

Other data format which are not mentioned in the report can be found here:

• BioSig
• scientific data formats

The Master Thesis

Title: Improvement of Response Time in SSVEP-based Brain-Computer Interface

author: Ignatius Sapto Condro Atmawan Bisawarna (That's me!)

supervisors: Prof. Dr.-Ing Axel Gräser, Dr.-Ing Ivan Volosyak & Thorsten Lüth, Dipl.-Ing.

year: 2010

place: Institute für Automatissierungstechnik (IAT), Universität Bremen, Bremen, Germany

about:

This master thesis is about how to make SSVEP-based BCI in the IAT faster (but with less error). A few time series prediction algorithms are then used. There were simulation with MATLAB, programming with C++, using BCI2000 platform and doing EEG experiments with human subjects. In the end, the proposed algorithms to help IAT system detect SSVEP faster are Regression method and Kalman Filter.

***

You can send email to saptocondro@ieee.org for more information and also the pdf files of my master project report and my master thesis.

***

The scientific paper mentioning me as an author

Title: BCI Demographics: How many (and what kinds of) people can use an SSVEP BCI?

author: B. Allison, I. Volosyak, T. Lüth, D. Valbuena, I. Sugiarto, M.A. Spiegel, A. Teymourian, I.S. Condro (That's me!), A. Brindusescu, K. Stenzel, H. Cecotti & A. Gräser.

Proc. 4th International Brain-Computer Interface Workshop and Training Course.

date: September 18-21, 2008

where: Graz, Austria

pages: 333-338

The scientific paper mentioning me in the acknowledgement

Title: BCI Demographics: How many (and what kinds of) people can use an SSVEP BCI?

author: B. Allison, T. Lüth, D. Valbuena, A. Teymourian, I. Volosyak & A. Gräser

journal: IEEE Transaction on Neural Systems and Rehabilitation Engineering

date: April 2010

volume: 18

number: 2

pages: 107-116

ISSN:1534-4320

The link to this paper can be found here.

***

I hope someday I can be an author in IEEE Transactions, especially the first author.

Improvement of Response Times in SSVEP-based Brain-Computer Interface

Starting on December 10th, 2009, I have a master thesis. The thesis should be submitted on May 27th, 2010. The presentation will be conducted in June (I hope).

The title of thesis is "Improvement of Response Times in SSVEP-based Brain-Computer Interface".

The supervisors are

• Prof. Dr.-Ing Axel Gräser
• Dr.-Ing. Ivan Volosyak
• Thorsten Lüth, Dipl.-Ing

The thesis is conducted in the Institute of Automation (IAT) at the University of Bremen.

The research group is no longer called BrainRobot. The name is now BRAIN, which stands for Brain-computer interfaces with Rapid Automated Interfaces for Nonexperts.

Yes, it is funded by European Union. We want to keep up with all research groups in the USA (and Canada) and in the Asia Pasific (China, Japan, Korea, etc).

Back to my thesis!

The proposed question behind the thesis is whether we can improve response times of our system in detecting SSVEP patterns from a subject. We can say that I want to make Bremen BCI system (a little bit) faster than before. I am using time-series manipulation algorithm to do so.

More details will be told in other blog posts.

Brain-Computer Interface definition, Allison et al, 2008

"Brain-computer interface (BCI) systems are devices that allow people to communicate without moving. Instead, direct measures of brain activity are translated into messages or commands."

From the paper:
B. Allison, I. Volosyak, T. Lüth, D. Valbuena, I. Sugiarto, M.A. Spiegel, A. Teymourian, I.S. Condro, A. Brindusescu, K. Stenzel, H. Cecotti and A. Gräser. 2008. "BCI Demographics I: How many (and what kinds of) people can use an SSVEP BCI?". Proc. 4th International Brain-computer Interface Workshop and Training Course. Graz, Austria, September 18th-21st. pp 333-338.

They are all from Institute of Automation (IAT), University of Bremen, Bremen, Germany.

You can also visit B. Allison's blog or I.S. Condro's blog (yes, that's me).
Some of us have Facebook:

• B. Allison
• D. Valbuena
• I. Sugiarto
• I. S. Condro

Brain-Computer Interface definition, Wolpaw et al, 2002

"A BCI is a communication system in which messages or commands that an individual sends to the external world do not pass through the brain's normal output pathways of peripheral nerves and muscles."

From the paper:
Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, Theresa M. Vaughan. 2002. "Brain-computer interfaces for communication and control". Clinical Neurophysiology. Ireland: Elsevier. Vol. 113, pp 767-791.

This paper has been cited by more than 1000 papers.

Jonathan R. Wolpaw is a Neuroscientist from the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health, Albany, New York and from the State University of New York, USA.

Niels Birbaumer is a Neurobiologist from the Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Tuebingen, Germany and from the Department of Psychophysiology, University of Padova, Padova, Italy.

Dennis J. McFarland and Theresa M. Vaughan are also from the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health, New York, USA.

Gert Pfurtscheller is from the Department of Medical Informatics, Institute of Biomedical Engineering, Technical University of Graz, Graz, Austria. Now in TU Graz, they have BCI Laboratory.

Vocabularies:

• BCI = Brain Computer Interface
• Padova = Padua
  

Brain-Machine Interface in the 19th century

Brain-Computer Interface (BCI) is not really a new technology as we can read from the news from IEEE Spectrum. There was a Head Set in the 19th century by pseudo-scientist, called "Phrenologist". The purpose of phrenology is to find correlation between a person's character and the morphology of the skull.

In one article of IEEE Spectrum, the picture number 2 shows the head set. It is mentioned like this:

HEAD CASE: Today’s electromedical researchers are busy mapping the brain, but 19th-century electrical engineers were already on the case. This electrical phrenology apparatus consists of two parts, a headpiece and a wooden box containing a sledge induction coil and three batteries. The headpiece forms a crown 23 centimeters (9 inches) in diameter with 13 brass electrodes evenly spaced across it.

From the picture, we can see the early research of Brain-Machine Interface. Well, it is not really a machine since the function is unknown. For more information about the history of Phrenology can be seen from their website.

Mind Reading Technology: the Mailing List

Can BCI be used for reading your mind?
The Germans would say "Jain" (Ja und Nein - Yes and No).

Yes, we can get your brainwave with EEG scanner and then recognize some patterns from the signals. We can also see wonderful 2D, 3D and "4D" patterns with MRI.

As a "Neuroscientist wannabe", I have been to a seminar showing that some phonemes can be "extracted" from EEG signals. So you don't have to move tongue and make a speech, the action of just thinking about a speech can be read by a machine. Scientists (Neuroscientists, Neurolinguists, and Engineers) have been doing this research. Maybe in the next 30 years, how the brain processes language can be interpreted by machine.

Human mind is so complex. Machine cannot really "read" your mind. Computers are able to read the patterns of brain signals. Only a few information from the human "mind" that can be extracted by machine.

I have found a mailing list in Yahoogroups which discuss mind reading technology. OK, it is not really a discussion. It is a monologue. Only one man posts and the other are just interested in reading the informations inside.

The site address:
http://tech.groups.yahoo.com/group/mindreadingtechnology/

The list has a link to some websites about mind "reading" technology.

The Next Uri Geller in CeBit 2008

In CeBit 2008, in Hannover, the IAT from Uni Bremen were placed in some corner. It is quite a convenient place. People went around passing that corner. That is the reason we could get 106 subjects in a week.

This video shows how I tested the spelling program before doing experiment with random subjects. You will see the stimulator with 5 flickering boxes: Left, Right, Up, Down and Select. In the middle, there are letters which we can choose to build a word. The hardwares used are notebook, EEG amplifier called g.USBamp from Guger Technologies, EEG electrodes and EEG cap. The news can be seen in Science Blogs.

Link:

FYI, Uri Geller is a mentalist who like to bend spoons. He is famous in Germany.
And The Next Uri Geller is a TV show in Germany.