1. Brain-Computer Interface systems based on the Steady-State Visual Evoked Potential
Presenter : Ching-Kai Huang
Adviser : Dr. Shih-Chung Chen
2017/4/26

2. Outline Background Introduction Literature Reviews
Materials and Methods
Results & Discussion
Future Works
References
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3. Background Four lobes of Cerebral cortex: Frontal lobe Parietal lobe
20 STROKECONNECTION July | August 2007
Frontal lobe
Parietal lobe
Temporal lobe
Occipital lobe
大腦半球的皮質共分為四葉，即額葉，顳葉，頂葉，和枕葉。
額葉frontal lobe顳葉temporal lobe枕葉occipital lobe頂葉parietal lobe
大腦皮質cerebral cortex
跟據EEG電位特徵的不同,電極量測的葉也會不同
比如說我現在要量測A法及b塔能量的強度, 那電極就要貼在額意葉以及頂葉的部位!!
Keywords: Cerebral cortex , Frontal lobe, Parietal lobe, Temporal lobe, Occipital lobe
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4. Background E.E.G(electroencephalogram):
An EEG records the electrical activity of the brain.
EEG features:
Voltage range: 0.5 ~ 100 μV
Frequency measure range: 0.5 ~ 100 Hz
The brain wave may be divided four basic waves:
α rhythm
β rhythm
θ rhythm
δ rhythm
腦波的形成是由大腦皮質部神經細胞電荷的移動而產生電場的變化所造成的!!
什麼是EEG?
就是是紀錄大腦活動時的電氣的活動
他的特點是電壓的範圍在0.5 ~ 100 μV
頻頻範圍在0.5 ~ 100 Hz
腦波可分為四種基本波形,分別為:α. β . Θ.δrhythm 一
從字義上來看
electro－electrical－電的；
encephalo－brain－大腦的；
gram(ma)－picture－圖像。
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5. Background The frequency and voltage range of four basic waves :
α (Alpha)
8~14 Hz；30~50μV;
β (Beta)
14~30 Hz；5 ~ 20μV ;
θ (Theta)
3.5~7 Hz；30μV <;
δ (Delta)
3.5 Hz<；100 ~ 200μV
依據頻率以及電壓的不同,可分為α (Alpha) 為8~14Hz頻段;電壓範圍則在30~50μV
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6. Introduction Brain-Computer Interface (BCI)
Several different features of scalp recorded EEG signals are being used as control signals.
Brain Computer Interface簡稱BCI,就是所謂的大腦人機介面
他的意思是,我們不需要透過肌肉或肌肉神經就可以達到後端控制的一種新穎性技術
示意圖如:特過電極量測到我的腦波訊號經過數位化之後傳到電腦端,做訊號特徵擷取及辨識的演算法分類
最後下達指令給我的後端控制,然後後端專至在freed back給受測者,顯示它的情況是如何?
Feedback
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7. Introduction Event Related Potential (ERP) is voltage fluctuations
that are associated in time with some physical or mental
occurrence.
Components of Event Related Potentials include:
μ and β Rhythm
P300 Evoked Potential
Steady-State Visual Evoked Potential
Slow Cortical Potential
事件相關電位(event-related potentials, ERPs )
他的定意是在特定時間內,身理或心理上發生變化,所產生的電壓波動
事件相關電位基本上有四個主要成份分別為:
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8. Literature Reviews
G. Pfurtscheller, C.Neuper, “Motor Imagery and Direct Brain-Computer Communication,” Proceedings of the IEEE, Vol. 89, , 2001.
Component of ERP: μ and β Rhythms
Donchin, E., Spencer, K. M. and Wijesinghe, R. S., “The mental prosthesis: Assessing the speed of a p300-based brain-computer interface,” IEEE Transactions on Rehabilitation Engineering, vol. 8, , 2000.
Component of ERP: P300 Evoked Potential
F. Beverina, G. Palmas, S. Silvoni et al., “User adaptive BCIs: SSVEP and P300 based interfaces,” PsychNology Journal, vol. 1, no. 4, , 2003.
Component of ERP: Steady-State Visual Evoked Potential
G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, “BCI2000: A general-purpose brain-computer interface (BCI) system,” IEEE Transactions on Biomedical Engineering, vol. 51, , 2004.
Component of ERP: Slow Cortical Potential
在這我摘錄的四篇四種 rep的成份分析及應用
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9. Literature Reviews μ and β Rhythms P300 Evoked Potential &
μ rhythms:10 ~ 12 Hz ； β rhythms:14 ~ 18 Hz
P300 Evoked Potential
This event related potential (ERP) appears as a positive deflection of the EEG voltage at approximately 300 ms.
Event-Related Synchronization
Event-Related Desynchronization
&
首先是μ and β Rhythms他的頻率範圍在10~12Hz ;
然後μ and β 是運用在支體運動及運動想像上面
那介由這張圖上面顯示,當我進行支體運動的時候,
準備進行支體運動前,他會產生一個電位抑制的現象,也就是μ
所謂的ERD,ERD就是事件相關非同步,那當我支體運動結束後,
我的電位會產生一個增強的現象,就是所謂的β~14~18 Hz,就稱為事件相關同步!!
這篇的運用在於,當我右手指動的時候,介面就會告訴我說,箭頭往右
P300 這篇文章所使用的是6X6矩陣的文字拼寫,以行列的方式來判段你的交集點在那
進而選出我要的單字.
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10. Literature Reviews Steady State Visual Evoked Potential (SSVEP)
These signals are natural responses for visual stimulations at specific frequencies.
Separate frequencies were used for each arrows. (6 Hz and 10 Hz)
簡單來說,刺激~胚特~為10Hz
那大腦會產生共同的震盪,也就會為10HZ
那如果你刺激閃爍刺激為6HZ,那在量測的頻域上大能量為6Hz ,
同時也會伴隨著12及18Hz,就是所謂的倍頻效應
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11. Literature Reviews Slow Cortical Potential (SCP)
These potential shifts occur over 0.5 ~ 10 s and are called slow cortical potentials.
慢速皮質誘發電位.他的電位位移大概發生在0.5~10s左右,
他通常是利用人的積極及消及來當做他的訊號特徵,
他是以一個球體來控制向上或向下,這個特徵必需訓練半年~一年之久,
才能達到8成或9成的準確率!!這個特徵越來越少人在使用!!
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12. Material and Methods Hardwares: Electrode caps NuAmps - amplifier
The hardwares include NuAmps amplifier, and the electrode caps , shown in Fig. 1 and Fig. 2.
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13. Material and Methods
The EEG recording for electrode cap with 37 channels, while the distribution of means using the International system of electrode placement.
波帽的電極總共有37個通道，
分佈的方式採用國際10 – 20 標準電極貼片位置
International system of electrode placement[5]
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14. Material and Methods Software:
The software I used to acquire brain wave is Scan 4.3 developed by NeuroScan company.
The other software I used to design human-machine interface is LabVIEW 2009 developed
by National Instruments company.
本系統所使用的軟體工具為NeuroScan公司開發的Scan4.3，還有美國國家儀器公司的LabVIEW2009版軟體程式語言
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15. Material and Methods Parameters Setup Sample rate: Filter:
1000 Hz
Filter:
Band Pass: 1Hz-30 Hz
Recording Channels: Oz
Reference: A2
Notch Filter:
60 Hz
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16. Material and Methods
ARM Wrestling:
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17. Material and Methods The 9 x 9 stimuli matrix: Start SSVEP BCI No
The threshold
Yes
Display
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18. Material and Methods The 9 x 9 stimuli matrix: Start SSVEP BCI No
The threshold
Yes
Display
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19. Material and Methods
Auto Power spectrum of SSVEP in response to 7 Hz stimulation.
Single Auto Power spectrum.
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20. Material and Methods
Auto Power spectrum of SSVEP in response to 10 Hz stimulation.
Single Auto Power spectrum.
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21. Material and Methods The 9 x 9 stimuli matrix: Start SSVEP BCI No
The threshold
Yes
Display
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If the threshold we defined, BCI system will output control command to the devices. Otherwise, BCI system will do nothing.

22. Results & Discussion
The game close beta testing.
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23. References
[1] Serby Hilit, Yom-Tov Elad, Inbar, Gideon F. “An improved P300-based
brain-computer interface,” IEEE Transactions on Neural Systems and
Rehabilitation Engineering, v 13, n 1, p 89-98, March, 2005
[2] Christoph Guger, Shahab Daban, Eric Sellers, Clemens Holzner, Gunther Krausz,
Roberta Carabalona, Furio Gramatica, Guenter Edlinger, “ How many people are
able to control a P300-based brain–computer interface (BCI)? ” Neuroscience
Letters, Volume 462, Issue 1, Pages 94-98, 18 September 2009
[3] Gray Garcia, "High frequency SSVEPs for BCI applications,"Computer-
Human Interaction 2008,April 2008,Florence,Italy
[4] 趙倫(2004)，ERP實驗教程，天津：天津社會科學院。
[5] Pfurtscheller G, Lopes da Silva FH (1999) Biomedical Engineering, Vol. 51, NO. 6
“Event-related EEG/MEG synchronization and desynchronization: basic principles,
Clinical Neurophysiology vol. 110,
[6] 基礎人體解剖與生理學
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24. References
[7] M. Cheng, X.R. Gao, S.K. Gao, and D.F. Xu, "Design and Implementation of a
Brain-Computer Interface With High Transfer Rates," IEEE Trans. Biomed. Eng.,
vol. 49, pp , 2002.
[8] G.R. Müller-Putz, R. Scherer, C. Brauneis, and G. Pfurtscheller, "Steady-state visual
evoked potential (SSVEP) -Based communication: Impact of harmonic frequency
components,"Journal of Neural Engineering, vol. 2, pp , 2005.
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25. Future works Papers review. Let the game interface complete.
改善訊號干擾及閥值的判斷方式
縮短判斷時間
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26. Thanks for your attention.
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27. 2017/4/26