透過您的圖書館登入
IP:3.140.185.147
  • 學位論文

使用前額穩態視覺誘發電位之腦波人機介面研究

A Frontal SSVEP-Based Brain Computer Interface

指導教授 : 李柏磊
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


本研究利用相位編碼閃光誘發出前額穩態視覺誘發電位(Steady -State Visual Evoked Potential, SSVEP)作為控制訊號,發展一套四選項腦機介面系統(Brain Computer Interface, BCI)。傳統視覺誘發電位大多量測於大腦皮質之枕葉區,電極之設置較不便且量測期間易受頭髮干擾,影響量測訊號品質。為了不讓頭髮干擾訊號、以及提升使用性,本研究將電極擺放至國際10-20system之FPz位置,提供一種新的穩態視覺腦波人機介面設計。 目前前額穩態視覺誘發電位的相關研究多數在探討其與認知領域的關聯性,並無利用前額穩態視覺誘發電位設計腦波人機介面的相關文獻,所以本研究先探討前額穩態視覺誘發電位設計腦波人機介面的可行性以定出腦波人機介面的閃光頻率以及閃光數目,也從中發現前額穩態視覺誘發電位較容易受眼動、眨眼訊號等其他雜訊之影響訊號品質,藉由配合適當的濾波器設計、配合眼動偵測與疊加平均技術,可成功將受到上述雜訊干擾的區段剔除並且濾除自發性腦波使得相位特徵更加穩定,最後藉由判斷使用者之前額穩態視覺誘發電位相位特徵得到使用者注視的閃光選項,使用者可根據其意念選擇要注視的閃光以輸入指令。 本研究徵召六位受試者,目前發展出的腦波人機介面已經可以達到四個指令的輸入,其平均指令傳輸速度可達7.35 second/command以及91%的準確率。

並列摘要


This study aim to develop a new brain computer interface (BCI), which is based on frontal Steady State Visual Evoked Potential (SSVEP) evoked by phase-tagged flashes in four light emitting diodes (LEDs).Traditional SSVEP-based BCI usually place electrodes on the scalp overlying occipital region. However, scalp around occipital area is usually covered with hair which requires longer setup time than non-hair bearing area and the contact impedance increases with the experiment time. Therefore, in order to achieve a SSVEP-based BCI for convenient use, the measurement of EEG electrode was moved to Fpz position in this study, referring to international EEG 10-20 system. Though several studies have been discussed about the relation between frontal SSVEP and cognitive functions, to our understanding, rare literatures were found in BCI applications. To investigate the possibility of frontal SSVEP in BCI use, we have first investigated the frequency-preference characteristics of frontal SSVEP and then evaluate the feasible flash number and flashing frequencies for BCI control. We found frontal SSVEP is more easily influenced by motion artifacts, such as eye blinks and eye movements. With proper rejection of artifact-contaminated SSVEP epochs, the frontal SSVEP can be stably obtained through band-pass filtering and epoch-averaging process. In our study, six subjects were recruited to sequentially input a command sequence, consisting of a sequence of four numbers, repeated twice. The accuracy and information transfer rate (mean ± SD) over the six subjects were 91.00 ± 7.68% and 12.36 ± 3.06 bits/min, respectively.

參考文獻


[2] J.-J. Vidal, “Toward direct brain-computer communication” , Annual review of Biophysics and Bioengineering, vol. 2, no. 1, pp. 157-180, 1973.
[3] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan,“Brain–computer interfaces for communication and control”, Clinical neurophysiology, vol. 113, no. 6, pp. 767-791, 2002.
[5] I. Volosyak, D. Valbuena, T. Malechka, J. Peuscher, and A. Gräser, “Brain–computer interface using water-based electrodes”, Journal of neural engineering, vol. 7, no. 6, pp. 066007, 2010.
[6] V. Mihajlovic, G. Garcia Molina, and J. Peuscher, "To what extent can dry and water-based EEG electrodes replace conductive gel ones?: A Steady State Visual Evoked Potential Brain-computer Interface Case Study."
[7] C. Guger, G. Krausz, B. Z. Allison, and G. Edlinger, “Comparison of dry and gel based electrodes for P300 brain–computer interfaces” , Frontiers in neuroscience, vol. 6, 2012.

延伸閱讀