Interactions - March/April 2009

machine interface (BMI). BMI achieves a literal realization of the human computer interaction paradigm by physically connecting man and machine. Over the past several years, BMI research has led to the development of brain-implantable chips that can translate a user’s neural impulses into a signal for controlling an external device, such as a robotic arm [ 11, 12]. Current state-of-the-art neuroprosthetic devices are far from the sleek biomorphic appendages featured in science fiction films, but are rather first-generation prototypes possessing minimal functionality. Although these technologies represent a remarkable step forward for amputees and other disabled persons, it is unlikely that healthy individuals will volunteer to undergo risky brain surgery simply for the potential interaction benefits. However, there is ongoing research to investigate the use of low-cost, noninvasive neural recording techniques—like electroencephalography (EEG)—as a basis for direct neural control of external devices [ 13]. These noninvasive devices may obviate the risks associated with implantable systems and provide a pathway for making BMI accessible to non-disabled users.

In fact, efforts to turn EEG into a sort of “BMI for the masses” are well under way, with at least two companies, Emotiv ( www.emotiv.com) and Neurosky ( www.neurosky.com), having developed EEG-based game controllers. It is unclear whether these stripped-down commercial systems (that feature far fewer electrodes than

[ 7] Chaminade, T., J. Hodgins, and M. Kawato. “Anthropomorphism influences perception of computer-animated characters’ actions.” Social Cognitive and Affective Neuroscience 2, no. 3 (2007): 206-216

[ 8] Bargh, J. A. and M.L. Ferguson. “Beyond Behaviorism: On the automaticity of higher mental processes.” Psychological Bulletin 126, no. 6 (2000): 925 -945.

[ 9] Itti, L., C. Koch, and
E. Niebur. “A model
of saliency-based
visual attention for rapid
scene analysis.” IEEE
Transactions on Pattern
Analysis and Machine
Intelligence 20, no. 11
(1998): 1254-1259.

• Figure 1: An icon’s salience, or “pop-out,” is determined in part by the properties of the display. On a white backgrou

a typical laboratory co tion) actually live up to marketing campaign. N activity patterns recor through EEG usually re slow changes in menta such as changing level attention and arousal. significant advances, it ly that gamers will be execute a rapid sequen actions (kick-punch-jum their thoughts alone. B we’ve seen with hacks Wii controller, placing device in the hands of _users _may _result _in _newnd (A), each icon is readily found; this is quantitatively captured by the model-generated salience map (in B). When pasted on a map (C), the same icons are far less salient (D). In D., white circles denote the position of each icon.

[ 10] Parasuraman, R. and G.F. Wilson. “Putting the brain to work: neuroergonom-ics past, present, and future.” Human Factors 50, no. 3 (2008): 468-474.

nfigura- ABOUT THE AUTHORS _the Brad Minnery is the neuro-technology group leader in ^eural The MITRE Corporation’s ded emerging technologies _flect office. His interests includ- _l _state, ed neurobiologically inspired intelligent systems and the cognitive neuroscience of ^s ^of human-machine interaction. He received Without his Ph.D. in neurobiology from the

[ 11] Schalk, G., K. J. Miller, N.R. Anderson, J. A. Wilson, M.D Smyth, J.G. Ojemann, D. W. Moran, J.R. Wolpaw, and E.C. Leuthardt. “Two-dimensional movement control using electrocorticographic signals in humans”. Journal of Neural Engineering 5, no. 1 (2008): 75-84.

_’s _unlike-University of Pittsburgh. ^able ^to Michael Fine is a senior ce of artificial intelligence engi- p) with neer at The MITRE _ut _as Corporation, where he studies the interaction of ^of ^the visual attention with learn- an EEG ing and memory. Fine has a Ph.D. in bio- eager medical engineering from Washington _innova-University, where he developed computational models to predict human motor

[ 12] Velliste, M., S. Perel, M.C. Spalding, A.S. Whitford, and A.B. Schwartz. “Cortical control of a prosthetic arm for self-feeding.” Nature 453, no. 7198, (2008): 1098-1101.

tive applications. As these and
behavior in virtual environments.

other neurally enabled technologies become more mainstream in the next decade, members of the HCI community should be ready to capitalize on their full potential.

[ 13] Wolpaw, J.R. and D. J. McFarland. “Control of a t wo-dimensional movement signal by a noninvasive brain-computer interface in humans.” Proceedings of the National Academy of Sciences of the United States of America 101 (2004): 17,849-17,854.