適用於中風復健之即時低複雜度腦機介面開發

Abstract

透過腦電波（EEG）的腦機介面（BCI）來幫助中風患者復健可以讓患者的腦波與外界產生連結、溝通，並恢復掌管運動腦區的功能。然而在傳統的方法中，需要大量的計算複雜度才能得到可信的偵測，並且無法達成即時反應。因此，本論文提出了適用於中風復健之即時低複雜度腦機介面開發。 本論文採用透過多頻帶空間濾波器（Frequency band common spatial pattern, FBCSP）來做為特徵擷取方法，並且為了節省計算複雜度，經由分析將EEG通道的數目由原本的19個通道降到只剩Fz, C3, Cz, C4，僅這4個通道來偵測正常人以及中風病人的運動想像意念。此外，頻帶數量也由原本的5個頻帶降到3個頻帶，4~7赫茲, 8~12赫茲, 13~30赫茲以節省計算複雜度。進一步為達成即時線上腦機介面，我們採用以上方法配合一秒取樣時間長度，並且搭配決定轉換區的緩衝方法來平滑我們的BCI控制。 最後，分析結果顯示我們降低將近9成的計算複雜度並在離線分析可以達到平均80%以上的正確率，且在線上即時分析的情況下，可以在1秒內偵測出受測者的運動想像意念，而正確率維持在平均67%。

Stroke rehabilitation with EEG-based brain computer interface enables interaction through brain signals and restoration of motor function of the brain. However, conventional approaches require high complexity for reliable detection and fail to achieve real time response. This thesis proposes a real time low complexity BCI interface for stroke rehabilitation. The proposed approach is based on the filter bank common spatial pattern (FBCSP) method. To reduce complexity, the EEG channels are reduced from 19 channels to 4 channels, Fz, C3, Cz, C4 to detect the movement intention for normal and stroke people with satisfying accuracy. Furthermore, the filter bank is reduced from five bands to three bands, 4~7Hz, 8~12Hz, 13~30Hz to reduce the complexity. A real time on-line scheme is developed with above method that uses one second time window for EEG analysis and transition region for smooth BCI control. These approaches saves 90% of computational complexity. The simulation results shows over 80% of accuracy for offline analysis, and 67% accuracy for the on-line approach with less than one second response time.