Two are better than one IEEE-SMC talk
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This document discusses the use of collaborative brain-computer interfaces (CBCIs) to improve visual target recognition, demonstrating that combining two brains enhances both reaction time (to 100ms) and accuracy (to 89.3%). The study highlights that individual brain EEG responses can distinguish between animal and distractor images, yet the integration of two brains significantly boosts performance. CBCIs show promise for applications in military operations and human-robot collaboration by leveraging multi-brain EEG signals for faster and more accurate decision-making.
Two are better than one IEEE-SMC talk
- 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