Milestone-Proposal:First Robotic Control from Human Brain Signals, 1988

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Docket #:2018-12

This Proposal has been approved, and is now a Milestone

To the proposer’s knowledge, is this achievement subject to litigation? No

Is the achievement you are proposing more than 25 years old? Yes

Is the achievement you are proposing within IEEE’s designated fields as defined by IEEE Bylaw I-104.11, namely: Engineering, Computer Sciences and Information Technology, Physical Sciences, Biological and Medical Sciences, Mathematics, Technical Communications, Education, Management, and Law and Policy. Yes

Did the achievement provide a meaningful benefit for humanity? Yes

Was it of at least regional importance? Yes

Has an IEEE Organizational Unit agreed to pay for the milestone plaque(s)? Yes

Has the IEEE Section(s) in which the plaque(s) will be located agreed to arrange the dedication ceremony? Yes

Has the IEEE Section in which the milestone is located agreed to take responsibility for the plaque after it is dedicated? Yes

Has the owner of the site agreed to have it designated as an IEEE Milestone? Yes

Year or range of years in which the achievement occurred:

1988

Title of the proposed milestone:

First Robotic Control from Human Brain Signals, 1988

Plaque citation summarizing the achievement and its significance; if personal name(s) are included, such name(s) must follow the achievement itself in the citation wording: Text absolutely limited by plaque dimensions to 70 words; 60 is preferable for aesthetic reasons.

In 1988, in the Laboratory of Intelligent Machines and Bioinformation Systems, human brain signals controlled the movement of a physical object (a robot) for the first time worldwide. This linked electroencephalogram (EEG) signals collected from a brain with robotics research, opening a new channel for communication between humans and machines. EEG-controlled devices (wheelchairs, exoskeletons, etc.) have benefitted numerous users and expanded technology's role in modern society.

200-250 word abstract describing the significance of the technical achievement being proposed, the person(s) involved, historical context, humanitarian and social impact, as well as any possible controversies the advocate might need to review.

IEEE technical societies and technical councils within whose fields of interest the Milestone proposal resides.

In what IEEE section(s) does it reside?

Region 8 Republic of Macedonia Section.Section Chair: Pero Latkoskiemail: pero@feit.ukim.edu.mk

IEEE Organizational Unit(s) which have agreed to sponsor the Milestone:

IEEE Organizational Unit(s) paying for milestone plaque(s):

Unit: Region 8, Republic of Macedonia Section
Senior Officer Name: Section Chair: Pero Latkoski

IEEE Organizational Unit(s) arranging the dedication ceremony:

Unit: Region 8, Republic of Macedonia Section
Senior Officer Name: Section Chair: Pero Latkoski

IEEE section(s) monitoring the plaque(s):

IEEE Section: Region 8, Republic of Macedonia Section
IEEE Section Chair name: Section Chair: Pero Latkoski

Milestone proposer(s):

Proposer name: Stevo Bozinovski
Proposer email: Proposer's email masked to public

Please note: your email address and contact information will be masked on the website for privacy reasons. Only IEEE History Center Staff will be able to view the email address.

Street address(es) and GPS coordinates in decimal form of the intended milestone plaque site(s):

18 Rugjer Boskovic street, 1000 Skopje, Macedonia Faculty of Electrical Engineering and Information Technologies GPS Coordinates: 42°00'17.6"N 21°24'30.0"E

Describe briefly the intended site(s) of the milestone plaque(s). The intended site(s) must have a direct connection with the achievement (e.g. where developed, invented, tested, demonstrated, installed, or operated, etc.). A museum where a device or example of the technology is displayed, or the university where the inventor studied, are not, in themselves, sufficient connection for a milestone plaque.

Please give the address(es) of the plaque site(s) (GPS coordinates if you have them). Also please give the details of the mounting, i.e. on the outside of the building, in the ground floor entrance hall, on a plinth on the grounds, etc. If visitors to the plaque site will need to go through security, or make an appointment, please give the contact information visitors will need. The building where the plaque will be placed has direct connection to the Milestone because in that building the Milestone was achieved. It is the Annex building of the main building of the Faculty of Electrical Engineering and Information Technology (FEEIT) in Skopje. The milestone was achieved in the Laboratory of Intelligent Machines and Bioinformation Systems which was in that building. There are no other historical markers on the site.

Are the original buildings extant?

Yes

Details of the plaque mounting:

t will be mounted on the wall of the hall at the entrance of the Annex building of the Faculty of Electrical Engineering and Information Technology (FEEIT), in which the lab resided at the time the Milestone was achieved.

How is the site protected/secured, and in what ways is it accessible to the public?

The place is inside a University building, so it is secured by the regular rules of entrance to the Annex of the FEEIT building. Usually students are passing by, as well as faculty members. The visitors of the plaque site do not need to go through security during the normal business hours.

Who is the present owner of the site(s)?

University “Sts Cyril and Methodius” in Skopje, MacedoniaFaculty of Electrical Engineering and Information Technologies in Skopje, Macedonia

What is the historical significance of the work (its technological, scientific, or social importance)? If personal names are included in citation, include detailed support at the end of this section preceded by "Justification for Inclusion of Name(s)". (see section 6 of Milestone Guidelines)

Historical significance

Before 1988, human EEG (electroencephalogram) signals and robotics were distant and mutually exclusive area of science and applications. Moreover, before 1988, controlling movement of a physical object using signals emanating from a human brain was in the realm of the science fiction. There was no idea of using engineering methods to address neither the possibility of psychokinesis nor the relation of EEG and robotics.

In 1988 it was shown for the first time that EEG research and robotics research are related by possibility of new communication channel between humans and robots. It was also shown that a psychokinesis can be achieved by engineering methods. As part of that, for the first time in the technological and scientific history, it was shown how movement of a physical object with a mass can be controlled using signals emanating from a human brain. It was shown how thoughts and absence of them can produce states of the brain which generate different EEG patters which can be used for control of a movement of a physical objects with a mass.

Those achievements, 1) establishing connection between EEG and robotics, as well as 2) solving the long lasting problem of engineering solution of psychokinesis, are most significant regarding scientific and technological importance of this achievement. Wider , social importance is that 3) another science fiction challenge became reality (examples of others are traveling to the moon, traveling below the ocean, etc).

Importance to the evolution of electrical and computer engineering and science

This achievement contributed to the evolution of the way robots communicate with humans. A new communication channel was opened, a direct brain communication with robots.

It also expanded the EEG research and application with a new challenge of controlling physical objects, like opening doors, driving drones, driving wheelchairs, using EEG only. Medical applications with paraplegic patients are also part of this evolution branch.

This achievement also contributed to the evolution of control circuits. It opened a road toward EEG-emulated control circuits. In the 1988 report a picture was shown of an EEG-emulated Schmidt trigger. This control switch recognized change in amplitude and frequency of an EEG signal and triggered corresponding robot control signal. Later in evolution, in 2015, it was shown how other control circuits such as flip-flop and demultiplexer can be EEG-emulated (reference from 2015 in the list of references below).

With that, a new evolutionary branch was opened in the science and engineering evolution tree. It is the evolution branch of EEG controlled physical devices. There is also a term in use for this evolution branch, and it is brain-machine interface. It requires a brain-to-computer interface and a computer-to-machine interface. It is not an universally accepted term, and we use the more descriptive term of control of movement of a physical objects using signals generated by a human brain. In this year, 2018, it is a 30th anniversary of opening this evolution branch. Current status is that almost every biomedical research and/or robotics laboratory now carries out such experiments. Wheelchairs and prostheses are being built and tested showing how paraplegic patients control them using brain signals. The game industry controls robots and drones using brain signals (e.g. Mindwave with Orbit helicopter, Emotiv with other devices). Pentagon is also experimenting with control of drones using brain signals. It is expected that this evolutionary path will be further followed.

Importance to regional/national/international development

It is a worldwide pioneering milestone achievement. The significance of the idea and achievement is, among other things, in how much it was ahead of time. The second achievement of control of movement of a physical object with signals generated by an animal brain was reported in 1999, eleven years after the 1988 achievement. The report was given by Nicolelis' group in USA (cited below). The 1988 and 1999 control of a physical object using signals from a brain, were the only ones achieved in the 20th century. Moreover the 1988 achievement was the only one using a human brain. In the 21st century there is an explosion of development and applications of moving physical objects using signals emanating from a human brain. Applications are medical, gaming, educational, military and others So it has significance in international development of a new technology for communication with robots and more generally in controlling movement of physical objects with a mass.

Benefits to humanity

The benefits to humanity can be seen in new research road as well as new applications for humanity. There are demonstrated applications in paraplegic subjects controlling physical objects. There are applications in entertainment industry with various drones controlled by the brain signals. It is also used in education labs, showing how students can control objects using own EEG signals.

The ways the achievement was a significant advance rather than an incremental improvement of existing technology.

This achievement was a significant advance rather than an incremental improvement of existing technology. The control of a physical object with a mass, using EEG generated intentionally by modulation of the state of the brain, was an unique achievement of solving the psychokinesis problem and opening a road to EEG control of wheelchairs, prostheses, home appliances, drones, and other physical objects. It was a pioneering achievement and opened a new road for science and technology.

What obstacles (technical, political, geographic) needed to be overcome?

Because it was a pioneering result in science and engineering, the most important obstacles were from conceptual and technical nature.

The most important obstacle was the challenge itself, of how to build a system that will demonstrate a psychokinesis control, control of a physical object using signals emanating from the human brain, willingly, by command of a subject. The subject should be able willingly to generate particular signal that will be emanated from the brain. The idea came to use EEG signals. The EEG is a representative of the signals emanating from a human brain. Actually from the brain to the electrodes, which are placed on the scalp outside the brain, the signals travel through various media, but not through wires. It is a wireless contact between the brain and the electrodes which, like antennas, collect signals emanated from the brain

The second part of the challenge was the physical object to be moved. The idea came to be a robot which is energy-autonomous. It would use onboard batteries to move the mass of the robot. EEG signals would be just control signals.

Basically, once the pioneering idea came as a conceptual solution, the other obstacles were of technological and engineering nature.

The second obstacle was the technological infrastructure. It was needed a robot moving space, which should be connected to a brain signal processing unit (biopotential amplifies, AD/DA converter, and a computer). That way EEG research will be related to robotic research. As solution, it was built a robot moving facility with two floors. One floor was robot arena, of size about a ping-pong table, where robots would move. The other, upper floor, was a unit containing a computer-to-robot interface, with power amplifiers for robot motors. That way the wires attached to robots came from above , not to pose an obstacle to the movement of the mobile robots in the arena. So, to overcome the infrastructure obstacle, a unique robot control setup was built, shown in attached Figures.

The third obstacle was to chose the way of encoding mental commands in the EEG signals. It was chosen the well known feature of EEG frequency band between 8 and 13 Hz, (also known as alpha or mu rhythm) to increase amplitudes if brain goes to a relaxation (idle) state. To achieve relaxation, the subject was instructed to close the eyes and relax, while EEG is measured from occipital (visual processing) area. So, when eyes are closed and visual brain relaxed, the EEG amplitudes as well as oscillation periods are higher, which is a command to the robot to move. When eyes are open to see how far the robot is from a predefined station, the visual brain is engaged, the EEG amplitudes and rhythm periods are lower, and the robot stops.

The fourth technical problem was a method of calibration (machine learning) the EEG parameters, which are different for each human subject engaged in controlling the robot. It was chosen a new method of learning two probability density distributions, rather than one, which was previously used in various other control problems. The two distributions were: the differences of both EEG amplitudes and time differences, between the extrema of the EEG signal amplitudes. This double-difference method proved successful in recognizing the events when EEG signal changes both its amplitude and the frequency, which is the case when EEG beta rhythm changes to EEG alpha rhythm and vice versa.

The fifth problem was choosing a demonstration robot and demonstration scenario. It was needed to find a robot that will have meaningful and interesting demonstration of a EEG-based control. A solution was found in the state-of-the-art toy robot from Elehobby, named Movit Line Tracer II which was able to follow a line drawn on the floor. It was a state of the art robot and it was purchased at the Akihabara market in Tokyo, Japan. It was controlled by a start/stop mechanical switch. It the lab, the switch was replaced by a EEG-emulated control switch. The robot was provided with a new outfit, to look like a shuttle robot in a Flexible Manufacturing System (FMS). The demonstration scenario was designed in which a robot moves along a closed circular (actually rectangular) line and was stopped/restarted at particular places ("FMS stations") by EEG encoded commands. That demonstration proved that psychokinesis (movement of a physical object using signals willingly generated from a human brain) was achieved by engineering methods. That way was shown how electrical and computer engineering solved another challenge of the science fiction.

What features set this work apart from similar achievements?

Firstly, it is its pioneering nature. Before 1988 there was no scientific work in relation between human brain signals and mechanical robots. This work also made a transition from science fiction to engineering. Many people taught of possibility of moving physical object with signals emanating from a human brain, but until 1988 nobody came to an idea to record EEG signals and build interface needed to control a movement of a physical object.

Second feature was the time period until the second such achievement was obtained. The 1988 achievement was reported at a IEEE conference in USA and at a conference in Croatia. In 1988 there was no World Wide Web, so it took 11 years till the next similar achievement was reported, in 1999 (Chapin J., Moxon K., Markowitz R., Nicolelis M., Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature Neuroscience 2, pp. 664–670, 1999). It was about a control of a physical object (a robotic arm) using signals recorded inside the brain of an animal, a rat. The first, 1988 achievement, recorded EEG non-invasively, with surface electrodes placed on a human scalp, while the second achievement in 1999, recorded invasively, with implanted electrodes inside an animal brain.

Another feature is time in history, the relation to the turn of the century. The mentioned 1988 and 1999 reports were the only ones describing control of physical objects with signals generated by a brain (both human and animal). The here described 1988 achievement was the only achievement using a human brain. So in the 20th century there were only one achievement of using human brain signals to control movement of a physical object. After the turn a century, in the 21st century, there are hundreds, possibly thousands of reports showing research and applications of this technology.

Why was the achievement successful and impactful?

Supporting texts and citations to establish the dates, location, and importance of the achievement: Minimum of five (5), but as many as needed to support the milestone, such as patents, contemporary newspaper articles, journal articles, or chapters in scholarly books. 'Scholarly' is defined as peer-reviewed, with references, and published. You must supply the texts or excerpts themselves, not just the references. At least one of the references must be from a scholarly book or journal article. All supporting materials must be in English, or accompanied by an English translation.

With this IEEE Milestone application are listed 27 reference documents which establish dates, locations, and importance of achievement: Nine of them are attached as media to this document. Eighteen are given by their links. Comments and excerpts are given with each document listed here.

1. S. Bozinovski, M. Sestakov, L. Bozinovska (1988) Using EEG alpha rhythm to control a mobile robot. In: G. Harris, C. Walker (eds.) Proceedings of 10th Annual Conference of the IEEE Engineering in Medicine and Biology Society, track 17: Biorobotics, New Orleans, LA, vol. 10, pp. 1515–1516

Comment: Edited book from the IEEE conference where the achievement was reported in 1988

Excerpt (page 1515) ... brain waves bioelectric control has not been widely studied areas although interest toward them was pointed out. This is one result of brain wave control of a robot, and the experimental design is chosen to be a meaningful one. We believe that this type of control can be applied in tasks where control is performed using only the signals from the head region, which is interesting for applications for handicapped, aircraft control, and space devices control, among others.

2. S. Bozinovski (1990) Mobile robot trajectory control: From fixed rails to direct bioelectric control. In O. Kaynak (Ed.) Proc IEEE International Workshop on Intelligent Motion Control, Istanbul, vol 2: 463-467

Comment: Edited book from the IEEE workshop where the algorithm of machine learning (Procedure Learning) and pattern recognition (Procedure Examination) was described, which was used in 1988 robot control using EEG signals.

Excerpt (page 465): EEG machine learning algorithm pseudo code: "Procedure Learning: Perform 10 sec Acquisition during which the operator has eyes open; Compute distributions for the time intervals between two extreme signal points, and the amplitudes of those points. Perform Procedure Learning replacing "eyes open" with "eyes closed". Compute decision border points for the pairs of distributions for "open" and "closed" case. "

3. A. Searle (2000) Electrode performance and signal processing strategies for the discrimination of EEG alpha waves: Implications for environmental control by unconstrained subjects without training. PhD Thesis. Department of Applied Physics, University of Technology, Sydney, Australia. https://opus.lib.uts.edu.au/bitstream/10453/19996/2/02Whole.pdf

Comment: Ph.D. Thesis. Mentions the 1988 and 1990 papers and methods described for machine learning and EEG pattern recognition.

Excerpt (page 21): In order to control a small mobile robot, Bozinovski et al. (1988) used a distribution of EEG peak values to decide whether the subjects eyes were closed or open (for example, if three successive peaks were inside the distribution space, the eyes were determined to be closed (Bozinovski 1990)).

4. S. Bozinovski (2013) Controlling robots using EEG signals, since 1988, In S. Markovski, M. Gusev (eds.) ICT Innovations 2012, Springer Verlag, p. 1-11, 2013

Comment: Edited book chapter from conference where, as plenary keynote paper, more detailed report of the achievement was presented.

Excerpt (page 1): "Telekinesis and psychokinesis are concepts with meaning of moving objects by utilizing energy emanating from a human brain produced by the brain mental processes. One approach to achieve such an effect is using a computer with two interfaces: one toward the brain for EEG signal processing, and the other toward a physical object for example a robot; with today’s technology either interface can be wireless. This paper is looking back to the first result of using EEG signals to control a movement of a physical object, a mobile robot, which was achieved in 1988 [1][2][3][4]." (here [2] is the 1988 paper)

5. S. Bozinovski, A. Bozinovski (2015) Mental states, EEG manifestations, and mentally emulated digital circuits for brain-robot interaction, IEEE Transactions on Autonomous Mental Development 7(1): 39-51

Comment: Journal paper mentioning the 1988 achievement and describing emulation of digital circuits using EEG signals. In addition to the EEG emulated switch used in 1988, EEG emulation of a flip-flop and a demultiplexer is also described in this paper.

Excerpt (page 41): An EEG switch is an EEG manifestation of a mental action in which a particular EEG feature changes in a way that it can be recognized by a computer. An example of an EEG switch is given in Fig. 3. It shows the EEG switch used in the 1988 BRI experiment [18], [19] (Here [18] is the reference to the 1988 paper).

6. M. Lebedev (2016) Augmentation of sensorimotor functions with neural prostheses. Opera Medica and Physiologica. Vol. 2 (3): 211-227

Comment: Journal paper, a review on neural prostheses, from a member of the team that in 1999 achieved second control of a robot using signals from a brain.

Excerpt ( page 213): "In 1988, the first report was published where human subjects controlled a robot with their EEG (Bozinovski, Sestakov et al. 1988). In that study, subjects issued binary commands by closing and opening their eyes. This maneuver started and stopped an alpha recorded with EEG sensors placed over the occipital cortex."

7. S. Bozinovski (2017) Signal processing robotics using signals generated by a human head: From pioneering works to EEG-based emulation of digital circuits, In A. Rodić and T. Borangiu (eds.), Advances in Robot Design and Intelligent Control, Advances in Intelligent Systems and Computing 540, Springer Verlag, p. 449-462

Comment: Edited book chapter from conference where, as plenary keynote paper, description was given on evolution of signal processing robotics, including speech signal for control of a mobile robots, and understanding human EEG messages.

Excerpt (page 454): "1988: Step in robot evolution: Robots gain capability of recognizing human EEG messages"

8. M. Lebedev, M. Nicolelis (2017) Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation, Physiological Review 97:737-867

Comment: Journal paper, a review on the brain-machine interface from the leader and a member the team which in 1999 made the second achievement of control of a robot using signals from a brain.

Excerpt (page 779): "It is worth noting that the first publication on a human controlling a robot with EEG activity dates back to 1988 (85)" where reference (85) is referencing the 1988 paper.

9. X. Liu, J. van der Spiegel (2018) Brain Machine Interface: , Springer Verlag.

Comment: A reference in a background book (textbook) in Brain Machine Interface. Points out the first control of a robot using brain signal.

Excerpt (Chapter 1, Introduction, page 1): The first demonstration of controlling a physical object using EEG signal was reported by S. Bozinovski in 1988 [7]." where [7] is referencing the 1988 paper.

10. A. Vijayendra, S. Kumaar, R. Vishwanath, S. Omkar (2018) A performance study of 14-channel and 5-channel EEG systems for real-time control of unmanned aerial vehicles (UAVs), Proc. IEEE International Conference on Robotic Computing, p. 183-188, Laguna Hills, California

Comment: Application of EEG control of a physical object, a flying robot. Controlling a drone using commercially available devices from the company Emotiv. Conference proceedings paper.

Excerpt (page 183): "Excerpt (page 183): "Soon after Vidal’s work, in 1988, Bozinovski et al. [3] reported a non-invasive EEG control of multiple start-stop-restart movements of a physical robot. Brain-computer interfaces, since then have been used for augmenting, assisting and repairing human cognitive or sensory-motor functions. As opposed to neuroprosthetics, BCIs are not artificial devices implanted in the human body." (here [3] is the 1988 reference)"

11. H. Ibadi (2018) A comprehensive approach for brain waves exploitation to control a robotic arm, IOSR Journal of Engineering, Vol. 2, pp 08-15, http://iosrjen.org/Papers/Conf.EQUINOX-2018/Volume-2/2.%2008-15.pdf

Comment. Journal paper. Application of brain waves based BCI to control a robotic arm.

Excerpt. (introduction, paragraph 1) Soon after Vidal‟s work, in 1988, Bozinovski et al. reported a non-invasive EEG control of multiple start-stop-restart movements of a physical robot.

12. X. Chen, B. Zhao, Y. Wang, S. Xu, X. Gao (2018) Control of a 7-DOF robotic arm system with an SSVEP-based BCI, International Journal of Neural Systems 28(8) 1850018 (15 pages), https://www.researchgate.net/publication/324484523

Comment: Journal article. An application of BCI for control of a robotic arm.

Excerpt (First page, paragraph 2): In order to solve this problem, brain-computer interface (BCI)-based control strategies were introduced into robot control [6-12], [where references 10-12 reference the 1988 work].

13. X. Zhang (2019) Practical electroencephalography (EEG) applications in stroke rehabilitation: towards brain-computer interface (BCI) setup and motor function assessment. PhD Thesis, School of Engineering Science, Faculty of Applied Sciences, Simon Fraser University, https://ir.lib.sfu.ca/item/19821 (link to abstract, then download pdf)

Comment: PhD Thesis. Application in medical rehabilitation. There are many applications if BCI for exoskeleton control for medical rehabilitation. For example, at the FIFA 2014 World Cup event, the initial kick of the ball was done by a paralyzed man wearing a BCI controlled exoskeleton.

Excerpt: The first application of EEG based BCI was reported in 1988, by Bozinovski et al. [76].[here [76] references the 1988 paper]

14. X. Wan, K. Zhang, S. Ramkumar, J. Deny, G. Emayavaramban, M. Siva Ramkumar, A. Hussein (2019) A review on electroencephalogram based brain computer interface for elderly disabled, IEEE Access, vol 7, https://ieeexplore.ieee.org/document/8671703

Comment: Journal paper. Application of BCI to rehabilitation of elderly, for example wheelchair control.

Excerpt: During 1990 EEG signals collected from the brain signals were converted to thoughts with the help of Brain Computer Interface (BCI) and EEG [1]-[7].[references 6 and 7 reference the 1988 paper, and the consequent 1990 paper]

15. T. Cattai, S. Colonnese, M.-C. Corsi, D. Bassett, G. Scarano, F. De Vico Fallani (2019) Combination of connectivity and spectral features for motor-imagery BCI. Proceedings of the 8th Graz Brain-Computer Interface Conference 2019, https://openlib.tugraz.at/download.php?id=5d7f7e6b02ac3&location=browse

Comment. Conference paper. Uses the frequency based BCI approach, as in 1988 paper.

Excerpt It allows communication [17] and the control of real or virtual objects [4]. [Here 4 references the 1988 paper]

16. S. Bozinovski, L. Bozinovska (2019), Brain–Computer Interface in Europe: the thirtieth anniversary, Journal for Control, Measurement, Electronics, Computing and Communications, Volume 60, 2019 - Issue 1, https://www.tandfonline.com/doi/full/10.1080/00051144.2019.1570644

Comment: Journal paper, a review of Brain-Computer Interface in Europe from the authors who performed the first BCI experiments in Europe.

Excerpt (page 40) "The original idea was to find an engineering solution of psychokinesis, the phenomenon of using energy emanating from a human brain to control movement of a physical object. The term “psychokinesis” appeared in science fiction and indeed till 1988 it was in the realm of science fiction. The engineering approach we implemented was to use EEG as representation of an energy emanating from a brain."

17. Emotiv (2019) The Introductory Guide to EEG (Electroencephalography). https://www.emotiv.com/eeg-guide/

Comment: Industry manual. Recognition from industry, from Emotiv, a leading supplier of Brain-Computer Interface equipment. It recognizes the brain control of a robot in 1988 as major achievement of EEG research in general, not just in a brain-machine interface domain.

Excerpt (page 9/25) "Stevo Bozinovski, Liljana Bozinovska and Mihail Sestakov were the first scientists to achieve control of a physical object using an EEG machine in 1988."

18. Emotiv (2019) The Introductory Guide to BCI (Brain-Computer Interface). https://www.emotiv.com/bci-guide/

Comment: Industry manual. Recognition from industry (Emotiv) that the first control of a robot using EEG signal in 1988 was a monumental experiment.

Excerpt (page 11/14) "A monumental 1988 experiment conducted by Stevo Bozinovski, Mihail Sestakov and Liljana Bozinovska used BCI and EEG to control a robot. The subject directed the robot to follow a line on the floor by sending brain signals from an EEG machine to BCI software connected to the robot. This experiment was the first to successfully control a physical object using an EEG machine."

19. P. Gaur, K. McCreadie, R. B. Pachori, H. Wang, G. Prasad (2019) Tangent Space Features based Transfer Learning Classification Model for Two-Class Motor Imagery Brain-Computer Interface, International Journal of Neural Systems 29(10) https://pure.ulster.ac.uk/ws/files/77556642/ws_ijns_revision7.pdf

Comment: Journal paper: pointing out the pioneering work of 1988.

Excerpt (page 2): “Bozinovska et al.[7, 8] were able to control a buzzer using their Contingent Negative Variation (CNV) potential, whilst Bozinovski et al. [9] were able to demonstrate robotic control using changes in the alpha frequency band for the very first time.” (here [9] is the 1988 paper)

20. S. Bozinovski (2020) EEG control, brain computer technology, and the arts. IEEE History Center, issue 113, July 2020

Comment: Article in IEEE History Center Newsletter. It is about using EEG control of a physical object in arts. Using EEG to control vibration of water and create an art exhibit. Article submitted by S. Bozinovski, edited by R. Colburn.

Excerpt (page 8): "Today, brain signal computer technology controls robots, drones, wheelchairs, prostheses, cars, exoskeletons, home appliances, and other physical and virtual objects. EEG control has also found a use in the arts. The artist Lisa Park uses the EEG connected to speakers, which vibrate water in vessels. She uses both brain waves and emotions computed from those brain waves to create original art and sound. Places she performed include Smithsonian Asian Pacific American Center, New York, NY, and South by South West Art, Austin, Texas."

21. The Royal Society (2019) iHuman: Blurring lines between mind and machine. https://royalsociety.org/-/media/policy/projects/ihuman/report-neural-interfaces.pdf

Comment: A book, ISBN: 978-1-78252-420-5 © The Royal Society, which is a study on neural interfaces. It contains a history chart pointing out the 1988 achievement as historical event in science. Royal Society is the oldest scientific academy in continuous existence, and in 1687 they published Newton's book Philosophiae Naturalis Principia Mathematica.

Excerpt (page 24): Timeline: "Ancient Egypt: Electric catfish used to treat arthritis; 1780s : Luigi Galvani of Bologna shows that muscle and nerve cells possess electrical force responsible for muscle contractions and nerve conduction; 1912: Ukrainian physiologist Vladimir Pravdich-Neminsky publishes the first animal electroencephalography (EEG); 1924: German physiologist and psychiatrist Hans Berger records the first human EEG; ... ...1988: EEG signals are used to control a mobile robot [64] (here [64] is the Bozinovski et al. reference); ... ...2016: Neuralink founded by Elon Musk and others to develop "ultrahigh bandwidth brain-machine interfaces to connect humans and computers"; 2017: Facebook reveals that it is working on wearable interfaces to enable people to type using brain signals alone. "

22. S. Bellary, D. Grabowsky, J. Conrad (2020) Indoor navigation for assistive robots using EEG signals as feedback. Proc IEEE SoutheastCon https://sunnybellary.com/publication/se19/se19.pdf

Comment: IEEE conference paper. Application of EEG control of a robot in assistive robotics.

Excerpt (page 1): "The use of EEG signals for controlling robotics has seen many implementations, dating back to pioneering works such with Bozinovski et al. [8] where a methodology was implemented for controlling a robot with EEG Alpha Rhythm signals."(here [8] is the 1988 reference]

23. M. Leroux (2020) How existing assistive HMIs could change our near future: Part II. https://www.kinovarobotics.com/en/knowledge-hub/how-existing-assistive-hmis-could-change-our-near-future-part-ii

Comment: The paper makes relation to science fiction, controlling machines with our minds. Application in assistive robotics.

Excerpt (page 1): "The power of thoughts. Controlling machines with our minds; it seems like the stuff of science fiction, right? You might be surprised to learn that in the research world, it is a very real possibility. In fact, controlling machines using electroencephalograms (EEG --> brain signals) was already accomplished in the 80s [19]" (here [19] is the 1988 reference}

24. Uploadme.org (2020): History of Brain-Machine Interface. https://uploadme.org/history_ascending.html

Comment: A timeline of historic events in history of brain-machine interface. The 1988 event is part of this timeline.

Excerpt (page 2/7): History: "1875: Discovery of electrical signals in animal brains. Richard Canton, a British physician and physiologist reported on 4th of August 1875 to the British Medical Association in Edinburgh that he had used a galvanometer to observe electrical impulses from the surfaces of living brains in the rabbit and monkey. 1924: Electroencephalogram (EEG). Hans Berger, a German psychiatrist, made the first EEG recording of human brain activity and called it Elektrenkephalogramm. ... ...1988: Brain-Robot-Interface. The first brain control over a robotic device using EEG is reported by Stevo Bozinovski, Liljana Bozinovska and Mihail Sestakov. ...2013: openbci.com. OpenBCI.com initiates as a community driven open-source platform for makers and consumers. ..."

25. S. Sharif and S. Ali (2020) “I felt the ball” --The future of spine injury recovery. World Neurosurgery 140: 602-613, https://researchgate.net/publication/341562599_I_felt_the_ball_-_Future_of_Spine_Injury_Recovery

Comment: Journal paper. About applications for helping patients with spinal cord injuries with EEG-controlled wheelchairs and exoskeletons.

Excerpt (page 606): "1988: Bozinovski et al. [46]: Human controlling a robot with EEG activity". Here [46] is the 1988 reference.

26. F. Ferracuti, S. Iarlori, Z. Mansour, A. Monteriù, C. Porcaro (2022) Comparing between different sets of preprocessing, classifiers, and channels selection techniques to optimise motor imagery pattern classification system from EEG pattern recognition, Brain Sciences, 12(1): 57, https://doi.org/10.3390/brainsci12010057

Comment: Journal paper: Pointing out the first use of frequency band in BCI after the Vidal (1973) defined the BCI, and also the first use of an Artificial Intelligence algorithm in a BCI containing a learning and a testing period.

Excerpt (page 1) "After BCI was defined by Vidal [5], the first use of a frequency band in a BCI was presented in 1988 [7,8]. .... The 1988 work on BCI robot control explicitly uses [9] an artificial intelligence (AI) algorithm with machine learning (training) period and examination (testing) period. " (Here [7] [8] and [9] address the 1988 paper)

27. Y. Li, T. Kesavadas (2022) SSVEP-Based Brain-Computer Interface for Part-Picking Robotic Co-Worker, Journal of Computing and Information Science in Engineering, 22 (2), 021001 , https://doi.org/10.1115/1.4051596

Comment: Journal paper: Recognizing that robots were controlled by EEG since 1988, referencing the 1988 paper about the first control of a robot using EEG.

Excerpt (page 1) "Since 1988, brain-computer interfaces (BCIs) have been built for controlling robots [2]. " (Here [2] references the 1988 paper)

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