1. EEG-controlled Robot and Interactive Technology Chairman: Dr.Hung-Chi Yang Presented by: :XUAN-JIA GUO Adviser: Prof. Shih-Chung Chen Date: Nov. 26, 2014 1

2. Outline Introduction Material and Methods Results Future Work References 2

3. Introduction Lou Gehrig's disease Physically disabled Cerebrovascular accident 3

4. Introduction Electroencephalogram(EEG) – Cerebral cortex Magnetoencephalographic(MEG) – Faraday's Law – Magnetic field 4

5. Material and Methods A total of 9 subjects Age: 20.77 ± 0.66 Bright lights Quiet 5

6. Material and Methods Began to experiment Stimulation frequency Count the number of times 30 times Eyes closed to rest 2 minutes Is five kinds of stimulation frequency completed? MAC analysis Relevance of each frequency domain End of the experiment YES NO 6

7. Results MSC Subject6Hz7Hz8Hz9Hz10Hz 10.3410.3740.3610.2690.323 20.4030.4460.5530.3600.364 30.3920.4170.4530.3900.377 40.3440.3610.3410.2830.277 50.2960.3870.3580.3170.294 60.3870.3860.3750.2920.285 70.3430.3400.4430.3460.329 80.3980.417 0.3210.303 90.3470.4140.3650.3360.272 7 Tab. 1 9 subjects’ average correlation

8. Results 8 Fig. 6 MSC spectral correlation averages

9. Results 9 Fig. 6 MSC spectral correlation averages

10. Results 10 Fig. 6 MSC spectral correlation averages

11. Results 11 Fig. 6 MSC spectral correlation averages

12. Results 12 Fig. 6 MSC spectral correlation averages

13. Results Similarity range ： 0.28 ～ 0.55 MSC Subje ct 6Hz7Hz8Hz9Hz10Hz 10.3410.3740.3610.2690.323 20.4030.4460.5530.3600.364 30.3920.4170.4530.3900.377 40.3440.3610.3410.2830.277 50.2960.3870.3580.3170.294 60.3870.3860.3750.2920.285 70.3430.3400.4430.3460.329 80.3980.417 0.3210.303 90.3470.4140.3650.3360.272 13 Tab. 2 9 subjects’ average correlation

14. Future Work 14 Fig. 7 Dry electrode cap

15. References Cecotti H (2011) Spelling with non-invasive brain- computer interfaces--current and future trends, J Physiology-Paris, vol. 105 no. 1-3, pp. 106-14. Mcfarland DJ and Wolpaw JR (2011) Brain-computer interfaces for communication and control, Commun ACM, 54:60-66. Chen S-C, Hong W-J, Chen Y-C, Hsieh S-C, and Yang S-Y (2010) The Page Turner Controlled by BCI, IFMBE Proceedings, 31:1534-1537. See AR, Chen S-C, Ke H-Y, Su C-Y and Hou P-Y (2013) Hierarchical Character Selection for a Brain Computer Interface Spelling System, INTECH2013 (Accepted) Duffy FH and H Als (2012) A stable pattern of EEG spectral coherence distinguishes children with autism from neuro- typical controls - a large case control study, BMC Med, 10: 64-81. 15

16. References H. Cecotti, “Spelling with non-invasive brain-computer interfaces--current and future trends,” Journal of Physiology - Paris, vol. 105 no. 1-3, pp. 106-14, 2011. F.-B. Vialatte, et al., “Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives,” Progress in Neurobiology, vol. 90, no. 4, pp. 418-438, 2010. “The Fundamentals of FFT-Based Signal Analysis and Measurement in LabVIEW and LabWindows/CVI” National Instruments, 2012. [Online]Available: http://www.ni.com/white- paper/4278/en/ [Accessed: 3 September 2013]. S.-C. Chen, A.R. See, Y.-J. Chen, et al. “The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device,” Mathematical Problems in EngineeringVolume, Vol. 2013, 2013. S.-C. Chen, A.R. See, C.-H. Yeng, et al. “Recreational Devices Controlled Using an SSVEP-based Brain Computer Interface (BCI),” Innovation, Communication and Engineering, pp. 175- 178, 2013. 16