以獨立成份分析應用於瞌睡偵測之即時嵌入式無線腦機介面系統

Abstract

近年來，有許多高速公路車禍發生的原因是由於駕駛者精神狀態不佳所造成。因此許多學者都在針對疲勞狀態偵測開發演算法。近年來，許多研究發現疲勞的特徵可以從眨眼的頻率、心跳頻率與腦波中萃取出來。這本研究中，我們開發一套基於腦波的量測與分析的疲勞偵測系統。而以腦波量測與分析的為主的系統最大的挑戰，就是腦波中的雜訊。腦波的量測與分析中不可避免的雜訊是眼動、眨眼、肌肉的雜訊、心跳的雜訊以及室電的干擾。而有研究證明獨立成分分析可以有效的移除多種的雜訊。然而大多數的獨立成份分析都是在電腦上進行離線分析而不是即時線上分析。 本論文以數位訊號處理器為基礎，搭配無線傳輸模組、生理訊號放大器，來實現一套即時可攜式無線腦機介面系統，包含三大發展主軸，分別為「腦電位訊號量測與無線傳輸」、「線上獨立成份分析演算法」及「即時疲勞狀態偵測與提醒演算法」。最後這套系統展示了連續並準確地偵測受測者的腦波中的精神狀態。

Many traffic accidents on the highway are caused by the driver’s inattention due to drowsiness. Hence, many researchers have devoted to develop algorithms to prevent drowsiness. Recently, several studies have shown that drowsiness related information is available in eye closures, heart rate and electroencephalogram (EEG). In this study, we developed a drowsiness detection system based on EEG recordings and data analysis. One of the biggest challenges in EEG-based system lies on the contaminations from inevitable EEG artifacts from eye movements, blinks, muscle, heart, and line noise. Independent component analysis (ICA) has been proven to be an effective technique to remove various types of artifacts. However, most of the ICA was performed offline on personal computers instead of an online analysis. Here, we design, develop and demonstrate an embedded wireless brain computer interface (BCI) including three functional blocks: EEG acquisition, amplification and wireless transmission, on-line ICA process and spectral analysis, and real-time drowsiness detection and feedback delivery to accurately and continuously detect and report subject drowsiness level based on the EEG data.