BRAIN-COMPUTER INTERFACE
(BCI)
Presentation
by
Raghuvarma Basavaraju
12/20/08
DCS860A
Agenda
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What is BCI?
BCI Disciplines
Why BCI?
BCI paradigms
Applications of BCI
Current trends and Future directions
References
What is a BCI?
• BCIs read electrical signals or other
manifestations of brain activity and translate them
into a digital form that computers can understand,
process, and convert into actions of some kind,
such as moving a cursor or turning on a TV.
• BCI can help people with inabilities to control
computers, wheelchairs, televisions, or other
devices with brain activity.
The 3 major components of BCIs
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[4]
Ways of measuring neural signals from the human brain
Methods and algorithms for decoding brain
states/intentions from these signals
and
Methodology and algorithms for mapping the decoded
brain activity to intended behavior or action.
Invasive versus Non-invasive BCI
• Invasive techniques, which implant electrodes directly onto
a patient’s brain;
• Noninvasive techniques, in which medical
scanning devices or sensors mounted on caps or headbands
read brain signals.
BCI Disciplines
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Nanotechnology
Biotechnology
Information technology
Cognitive science
Computer science
Biomedical engineering
Neuroscience
Applied mathematics
[1]
Why BCI? [2]
• BCI is a new neuroscience paradigm that might help us better
understand how the human brain works in terms of reorganization,
learning, memory, attention, thinking, social interaction,
motivation, interconnectivity, and much more.
• BCI research allows us to develop a new class of
bioengineering control devices and robots to provide daily life
assistance to handicapped and elderly people.
• Several potential applications of BCI hold promise for
rehabilitation and improving performance, such as treating
emotional disorders (for example, depression or anxiety), easing
chronic pain, and overcoming movement disabilities due to
stroke.
• BCI can expand possibilities for advanced human computer
interfaces (HCIs), making them more natural, flexible, efficient,
secure, and user-friendly by enhancing the interaction between
the brain, the eyes, the body, and a robot or a computer.
BCI Paradigms
[2]
• Passive endogenous: specific mental imagination
activity— for example, motor imagery or mental
arithmetic;
• active endogenous: active neurofeedback and unrestricted
mental imagination using the operant-conditioning
principle—a “no specifics” cognitive, “just do it” principle;
• passive exogenous: responses to externally driven stimuli
to evoke specific brain responses called event-related
potentials (ERPs); and
• active exogenous: consciously modified responses to
external stimuli, often combined with neurofeedback.
Carleton University’s proposed BCI-based biometric system [1]
Subjects use specific thoughts as passwords (called pass-thoughts).When someone
tries to access a protected computer system or building, they think of their passthought.A headpiece with electrodes records the brain signals.The system
extracts the signal’s features for computer processing,which includes
identification of the feature subset that best and most consistently represents the
pass-thought.The biometric system then compares the subset to those recorded
for authorized users.
Current and future trends
in noninvasive BCI
[2]
• Unimodal to multimodal - that is, simultaneous monitoring
of brain activity using several devices and combining BCI
with multimodal HCIs;
• Simple signal-processing tools to more advanced machine
learning and multidimensional data mining;
• Synchronous binary decision to multidegree control and
asynchronous self-paced control;
• Open-loop to closed-loop control - neurofeedback
combined with multimodal HCI; and
• Laboratory tests to practical trials in the noisy real world
environment.
References
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5.
Sixto Ortiz Jr., "Brain-Computer Interfaces: Where Human and Machine
Meet," Computer, vol. 40, no. 1, pp. 17-21, Jan., 2007
Andrzej Cichocki, Yoshikazu Washizawa, Tomasz Rutkowski, Hovagim
Bakardjian, Anh-Huy Phan, Seungjin Choi, Hyekyoung Lee, Qibin Zhao,
Liqing Zhang, Yuanqing Li, "Noninvasive BCIs: Multiway SignalProcessing Array Decompositions," Computer, vol. 41, no. 10, pp. 34-42,
Oct., 2008
Anton Nijholt, Desney Tan, Gert Pfurtscheller, Clemens Brunner, Jos del
R. Mill, Brendan Allison, Bernhard Graimann, Florin Popescu, Benjamin
Blankertz, Klaus-R. M?, "Brain-Computer Interfacing for Intelligent
Systems," IEEE Intelligent Systems, vol. 23, no. 3, pp. 72-79, May/Jun,
2008
1. F. Babiloni, A. Cichocki, and S. Gao, eds., special issue, “BrainComputer Interfaces: Towards Practical Implementations and
Potential Applications,” ComputationalIntelligence and
Neuroscience, 2007;
P. Sajda, K-R. Mueller, and K.V. Shenoy, eds., special issue, “Brain
Computer Interfaces,” IEEE Signal Processing Magazine,Jan. 2008.