A System-on-Chip Design of 32-Channel EEG Acquisition System with Automatic Artifacts Rejection
|關鍵字:||腦波擷取;線上遞迴式;獨立通道成分分析;肌電雜訊;及時處理;系統晶片設計;EEG acquisition;Online recursive;Independent component analysis;Artifacts;real-time;Soc;BSS-CCA|
線上遞迴式獨立成分分析這樣的演算法，提供了我們一個即時雜訊萃取的機制，能在每次腦波訊號由前端擷取器完成取樣後，隨即完成獨立通道成分分析的演算，因此非腦內成分的雜訊能夠及時的被萃取出來。因為這樣即時萃取雜訊的特性, 再經由 (Blind source separation- Canonical Correlation Analysis, BSS-CCA) 進行肌電及眼動雜訊的去除, 即可將乾淨且不受干擾的腦波傳輸到腦機介面中進行處理, 達到更精確的判讀.
此演算法設計於TSMC 40 nm COMS 製程，並實現於FPGA上。每個腦波訊號能於取樣後的0.27秒內得到不含肌電雜訊的腦波訊號，在此論文中我們亦提供了評估此系統效能的方式，針對在消除肌電與眼動雜訊的效能上，與原始腦波的平均相關度為0.9341 和 0.8927，另外我們亦使用真實的腦波進行處理，結果顯示，經過此系統的處理後，雜訊確實的被去除。|
This thesis presents a system-on-chip design of a 32-channel EEG acquisition system with an automatic artifacts rejection. EEG signals are among the feeblest physiological electrical signals we can capture from the human body and are easily contaminated with artifacts caused by noncerebral electrical activities. Previously, ICA was used to extract artifacts from a period of EEG data, and after processing ICA, An automatic artifacts detection and elimination algorithm is performed to remove the artifacts from EEG signals and free EEG signals can be efficiently reconstructed. Nowadays, brain-computer interfaces (BCIs) are being developed to control machines through EEG analysis directly. To enhance the accuracy, and feasibility, reliability of BCIs, EEG signals used for BCI applications should be acquired in real-time from the human scalp without artifacts. The real-time requirement is achieved by adopting online recursive ICA (ORICA) which can help extract artifacts in real-time as it can immediately produce ICA results right after each EEG sample acquisition. Then through BSS-CCA (Blind source separation- Canonical Correlation Analysis) we can remove the artifacts and get a clean and undisturbed EEG signal ready to be processed by brain-computer interfaces with better and more accurate interpretations. There are two kinds of artifacts. The first type originates from the inside of the human body and is referred as biological artifacts, typically muscle artifact known as EMG artifact. The second one is due to the surrounding environment, from outside of the human body, and is referred as environment artifact. It is known that EMG of skeletal muscles recorded from the skin has a broad frequency distribution from 0 to >200 Hz with several more or less distinct spectral components. EMG activity affects alpha, beta, and even delta frequency bands. However, most automated methods for EMG artifact detection and elimination make the assumption that EMG recorded from the scalp has a broad spectral distribution that begins at 15–20 Hz. Therefore, both artifacts from frontalis and temporalis muscles are the ones we focused on. These two muscles are the most common sources of EMG artifacts over frontal and central head regions when recording EEG signals. They must be removed to avoid any misleading in the operation of BCIs. The system is designed to include these algorithms using TSMC 40nm CMOS technology and implemented on FPGA. With the proposed multi-channel processing flow, multi-channel artifact free EEG signals ca be acquired in 0.27 s after input sample. The performance of eye blink artifact and muscle artifact elimination is evaluated through the computation of correlation coefficients between original artifact free EEG signals and processed artifact free EEG signal which are 0.9341 and 0.8927 averagely. The processed results with real EEG signals are also shown to remove muscle artifacts successfully.
|Appears in Collections:||Thesis|