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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

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Outline

• Introduction• Material and Methods• Results• Future Work• References

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Introduction

• Lou Gehrig's disease• Physically disabled• Cerebrovascular accident

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Introduction

• Electroencephalogram(EEG)– Cerebral cortex

• Magnetoencephalographic(MEG)– Faraday's Law– Magnetic field

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Material and Methods

• A total of 9 subjects• Age: 20.77 ± 0.66• Bright lights• Quiet

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Material and MethodsBegan 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

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Results

　 MSC

Subject 6Hz 7Hz 8Hz 9Hz 10Hz

1 0.341 0.374 0.361 0.269 0.323

2 0.403 0.446 0.553 0.360 0.364

3 0.392 0.417 0.453 0.390 0.377

4 0.344 0.361 0.341 0.283 0.277

5 0.296 0.387 0.358 0.317 0.294

6 0.387 0.386 0.375 0.292 0.285

7 0.343 0.340 0.443 0.346 0.329

8 0.398 0.417 0.417 0.321 0.303

9 0.347 0.414 0.365 0.336 0.272

Tab. 1 9 subjects’ average correlation

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Results

Fig. 6 MSC spectral correlation averages

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Results

Fig. 6 MSC spectral correlation averages

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Results

Fig. 6 MSC spectral correlation averages

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Results

Fig. 6 MSC spectral correlation averages

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Results

Fig. 6 MSC spectral correlation averages

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Results

• Similarity range： 0.28～ 0.55

　 MSCSubject 6Hz 7Hz 8Hz 9Hz 10Hz

1 0.341 0.374 0.361 0.269 0.323 2 0.403 0.446 0.553 0.360 0.364 3 0.392 0.417 0.453 0.390 0.377 4 0.344 0.361 0.341 0.283 0.277 5 0.296 0.387 0.358 0.317 0.294 6 0.387 0.386 0.375 0.292 0.285 7 0.343 0.340 0.443 0.346 0.329 8 0.398 0.417 0.417 0.321 0.303 9 0.347 0.414 0.365 0.336 0.272

Tab. 2 9 subjects’ average correlation

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Future Work

Fig. 7 Dry electrode cap

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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.

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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.