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Prolog to the Section on Neurotechnological Systems: The Brain-Computer Interface

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CENTENNIAL SECTION PROLOG

Prolog to the Section on

Neurotechnological Systems:

The Brain–Computer Interface

B

Y

C

H I N

- T

E N G

L

I N

,

Fellow IEEE

,

A N D

K

A L E B

M

C

D

O W E L L

, Senior Member IEEE

As the proliferation of technology dramatically infiltrates all aspects of social life, engineering will continue to in-tertwine the human brain with technology, thus forming integrated neurotechnological systems. Major forerunners of such a conception are brain–computer interfaces (BCIs), which are based on a direct communication path-way between the human brain and an external device. First developed in the 1970s, BCIs have been largely focused on improving the quality of life of particular clinical popula-tions and include, for example, advanced communicapopula-tions with locked-in patients and the direct control of prostheses and wheelchairs. Over the past five years there has been an explosion of research and development into technologies underlying the use of online brain–signal processing to influence human interactions with computers, their envi-ronment, and even other humans,

i.e., BCI technologies, which has even led to the commercialization of the first brain-based toys. Over the next decades, BCI technology will expand beyond integration in med-ical and laboratory settings and into everyday life.

In this section of the Centennial Special Issue, we focus on current and potential BCI technologies and research enabled by recent advances in wearable, mobile biosensors and data acquisition; neuroscience; computational and analytical ap-proaches; and computing for brain imaging in real-world environments. In the first paper, Liao et al. discuss barriers to taking brain imaging sys-tems out of laboratory and clinical settings and into everyday

environ-ments, and highlight current and future approaches to address those barriers. This paper focuses on recent and projected advances of a wide range of sensor and acquisition neurotechnologies enabling online brain– signal processing in everyday, real-life environments. In the second paper, Makeig et al. discuss the challenges associated with building robust and useful BCI models from accumulated biological knowledge and available data, and the technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may become ubiquitous in the future. This paper focuses on recent advances and current trends in signal processing of electroencephalography (EEG) data and future approaches to processing EEG in combination with multimodal sources of data. One of the primary benefits of the neurotechnologies discussed in the first two papers is that they are envisioned to enable researchers to experiment using naturalistic tasks and in real-world environments to produce a much deeper and perhaps very different understanding of the link between behavior and biology; an understanding which may dra-matically influence BCI technologies as well as the broader neuroscience community. In the third paper, Lance et al. discuss the potential of using online brain–signal processing to enhance human–computer inter-actions and the barriers to realizing this potential. This paper discusses past and current BCI applications and proposes future BCI technologies that will make significant expansion into training, education, entertainment, rehabilitation, and human–system performance domains. These technologies include novel user-acceptable inter-faces to monitor brain function and human behaviors in real-world environments.

The vast growth in neuroscience research over the past several decades presents a remarkable opportunity to synthesize and leverage this knowledge base for improving

This special section

on neurotechnological

systems focuses on

current and potential

brain–computer interface

(BCI) technologies and

research enabled by

recent advances in

wearable, mobile

biosensors and data

acquisition as well as

advances in neuroscience

and computing for brain

imaging and related fields.

C.-T. Lin is with the Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan, the Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan (e-mail: ctlin@mail.nctu.edu.tw).

K. McDowell is with the Translational Neuroscience Branch, Army Research Laboratory, Aberdeen Proving Ground, MD USA (e-mail: kaleb.g.mcdowell.civ@mail.mil). Digital Object Identifier: 10.1109/JPROC.2012.2187137

Vol. 100, May 13th, 2012 |P r o c e e d i n g s o f t h e I E E E 1551 0 0 1 8 - 9 2 1 9 / $ 3 1 . 0 0Ó2 0 1 2 I E E E

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human–system technologies. In other words, neuro-sciences and neurotechnologies offer an opportunity to revolutionize human–system performance by integrating modern neuroscience with human factors, cognitive science, computer science, materiel development, and engineering to enhance our understanding of human

function in complex real-world settings and develop novel and effective systems design. With increasing technology development, in many ways the world is becoming more dynamic and complex. It now becomes critical to design and develop flexible and adaptive systems that integrate with, and capitalize on humans’ abilities and limitations.h

A B O U T T H E A U T H O R S

Chin-Teng (CT) Lin (Fellow, IEEE) received the B.S. degree in control engineering from National Chiao-Tung University (NCTU), Hsinchu, Taiwan, in 1986 and the M.S.E.E. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, IN, in 1989 and 1992, respectively. Since August 1992, he has been with the College of Electrical Engineering and the College of Computer Science, NCTU, where he is the Provost and the Lifelong Chair Professor of the

Department of Electrical Engineering. He served as the Founding Dean of Computer Science College of NCTU from 2005 to 2007. He is the author of the textbook Neural Fuzzy Systems (Englewood Cliffs, NJ: Prentice-Hall) and Neural Fuzzy Control Systems with Structure and Parameter Learning (Singapore: World Scientific). He has published over 165 journal papers, including about 77 IEEE Transactions papers. His research interests are in translational neuroscience, computational intelligent technologies, soft computing, brain–computer interface, smart living technology, intelligent transportation systems, robotics and intelligent sensing, and nanobioinformation technologies and cognitive science (NBIC).

Dr. Lin was elevated to IEEE Fellow in 2005 for contributions to biologically inspired information systems. He was honored with Out-standing Electrical and Computer Engineer (OECE), Purdue University, in 2011. He was a member of the Board of Governors (BoG) of the IEEE Systems, Man, Cybernetics Society (SMCS) from 2003 to 2005 and IEEE Circuit and Systems Society (CASS) (2005–2008), and is the current AdCom member of IEEE Computational Intelligence Society (CIS) (2008– 2010). He was the IEEE Distinguished Lecturer from 2003 to 2005. He currently serves as the Editor-in-Chief (EIC) of the IEEE TRANSACTIONS ON

FUZZYSYSTEMS. He was an Associate Editor of the IEEE TRANSACTIONS ON

SYSTEMS, MAN,ANDCYBERNETICSVPARTA: SYSTEMS AND HUMANS. He also served as the Deputy EIC of the IEEE TRANSACTIONS ON CIRCUITS AND

SYSTEMSVPARTII: EXPRESSBRIEFSfrom 2006 to 2007. He is the General Chair of FUZZ-IEEE 2011 held in Taipei, and was the Program Chair of the

2006 IEEE International Conference on Systems, Man, and Cybernetics held in Taipei. He was the President of the Board of Government (BoG) of Asia Pacific Neural Networks Assembly (APNNA) from 2004 to 2005. He has the Outstanding Research Award granted by the National Science Council (NSC), Taiwan, since 1997 to present, the Outstanding Professor Award granted by the Chinese Institute of Engineering (CIE) in 2000, and the 2002 Taiwan Outstanding Information-Technology Expert Award. He was also elected to be one of the 38th Ten Outstanding Rising Stars in Taiwan (2000). He is a member of Tau Beta Pi, Eta Kappa Nu, and Phi Kappa Phi honorary societies.

Kaleb McDowell (Senior Member, IEEE) was born in Frederick, MD, on July 10, 1970. He received the B.S. degree in operations research and industrial engineering from Cornell University, Ithaca, NY, in 1992 and the M.S. degree in kinesiology and the Ph.D. degree in neuroscience and cognitive sci-ence from the University of Maryland, College Park, in 2000 and 2003, respectively.

He is currently the Chief of the Translational Neuroscience Branch and Chair of the

Neurosci-ence Strategic Research Area at the U.S. Army Research Laboratory (ARL), Aberdeen Proving Grounds, MD. Since joining ARL as a Research Psychologist in 2003, he has contributed to over 40 reviewed publica-tions, and has led several major research and development programs focused on neuroscience, indirect vision systems, and vehicle mobility. His current research interest focuses on translating basic neuroscience into applications for use by healthy populations in everyday, real-world environments.

Dr. McDowell received Department of Army Research and Develop-ment AchieveDevelop-ment awards for technical excellence in 2007 and 2009 and the ARL Award for Leadership in 2011.

Prolog to the Section on Neurotechnological Systems: The Brain–Computer Interface

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