K Yun, A Stoica, “Improved target recognition response using collaborative brain computer interfaces” IEEE SMC 2016

1. Two are better than one: improved
target recognition using collaborative
brain computer interfaces
Kyongsik Yun and Adrian Stoica
Robotic Systems Estimation, Decision, and Control group

2. Each human has different visual
search pattern
It’s a cat!
Reaction time: 280ms
Accuracy: 81.2%
It’s a cat!
Reaction time: 230ms
Accuracy: 79.4%
- Some search from left to right and others search from right to left.
- Some are more efficient in specific targets (e.g., cats) and some are not.
- How about combining two brains for improved visual target recognition?

3. Target (animal) and distractor (non-animal)
images used in the Go/No-go categorization
The participants were instructed to press the go button as soon as they
recognized the target images

4. Experimental procedure of one-
brain and two-brains EEG analysis
EEG
EEG
EEG data
fusion
SVM
classifier
SVM
classifier
One-brain
(79.4%)
Two-
brains
(89.3%)

5. Support Vector Machine
• A discriminative classifier
formally defined by a
separating hyperplane
• Given labeled training
data (supervised learning),
the algorithm outputs an
optimal hyperplane which
categorizes new examples
¡ Animal
o Distractor

6. Single brain EEG response showed that we can
differentiate between animal and distractor
images in 300ms. Can we improve it?
(gray area: false discovery rate multiple
comparisons corrected p<0.05)

7. Combining two brains can detect animal images
in 100ms that is 200ms improvement
The classification accuracy of two and three brains are significantly higher
than that of one brain.

8. It’s a cat!
Reaction time: 280ms
Accuracy: 81.2%
It’s a cat!
Reaction time: 230ms
Accuracy: 79.4%
It’s a cat!
Reaction time: 100ms
Accuracy: 89.3%
Two brains
data fusion
One brain
One brain
Visual search
trajectory
Collaborative brain computer interfaces using two brains fusion data
significantly improve the accuracy and the reaction time of the task

9. Cooperative brain computer interfaces
(cBCI)
• BCI is a direct communication
pathway between a wired
brain and an external device
• cBCI is a direct
communication pathway
between two brains through
an external device
• cBCI accelerates the assisting,
augmenting, or repairing
human cognitive or sensory-
motor functions
Stoica EST 2012,
Stoica et al. SPIE 2013
Yun et al. Sci. Rep. 2012,
Yun J. Neurosci. 2013

10. Conclusion
• One can achieve higher levels of perceptual
and cognitive performance by leveraging
the power of multiple brains through
collaborative brain-computer interfaces
• We can integrate multi-brain EEG signals to
obtain faster, more accurate cognitive
decision making to achieve high
performance brain-computer interfaces
• It can be applied to military operations and
human-robot collaborations

11. References
• K Yun, A Stoica, “Improved target recognition response using
collaborative brain computer interfaces” IEEE SMC 2016
• D. Chung, K. Yun, and J. Jeong, "Decoding covert motivations of free
riding and cooperation from multi-feature pattern analysis of EEG
signals," Social cognitive and affective neuroscience, 2015
• A. Stoica, A. Matran-Fernandez, D. Andreou, R. Poli, C. Cinel, Y. Iwashita,
and C. Padgett, "Multi-brain fusion and applications to intelligence
analysis," in SPIE Defense, Security, and Sensing, 2013
• K. Yun, "On the same wavelength: Face-to-face communication increases
interpersonal neural synchronization," The Journal of neuroscience, 2013
• R. Poli, C. Cinel, F. Sepulveda, and A. Stoica, "Improving decision-making
based on visual perception via a collaborative brain-computer interface,"
in Cognitive Methods in Situation Awareness and Decision Support
(CogSIMA), 2013 IEEE International Multi-Disciplinary Conference on,
2013
• A. Stoica, "Aggregation of bio-signals from multiple individuals to achieve
a collective outcome," ed: Google Patents, 2012
• K. Yun, K. Watanabe, and S. Shimojo, "Interpersonal body and neural
synchronization as a marker of implicit social interaction," Scientific
Reports, 2012