基於穩態子空間偵測大腦狀態變換

Abstract

大腦的狀態變化像是從清醒狀態到疲勞狀態，釀成了許多不幸發生。為了避免因生理變換導致的悲劇重演，因此偵測狀態變換是個很重要的議題。先前研究提到了狀態變換與腦電圖的非穩態特性有個緊密關係。本研究除了分析腦電圖的非穩態特性，也運用穩態子空間演算法提出一個參考指標來偵測大腦狀態變換。此外，為了全面瞭解隨著狀態改變對應的動態變化，我們分析了持續性駕車任務資料集和睡眠資料集。在駕車任務中，我們發現清醒狀態的駕駛者比疲勞狀態的駕駛者腦中有更多的穩態成分，而駕車反應時間也與穩態成分個數呈現顯著負相關。另一方面，大腦穩態成分和非穩態成分的β頻帶能量與反應時間呈現顯著負相關。在睡眠資料集，受測者在清醒狀態下的穩態成分比N1, N2, N3睡眠期還要多，而穩態成分在快速動眼期反增。另外，穩態成分在受測者進入深層睡眠時是與睡眠階段呈現負相關，然而進入深層睡眠後則呈現出正相關。本研究提出的指標可以用來偵側大腦的狀態變換，這樣的發現可以大量減少因為大腦狀態變換而造成的交通事故發生。

Brain state drift, such as from alertness to drowsiness, leads to numerous miserable incidents. In order to prevent the tragedy resulting from human state changes happens in the future, detecting the state drift becomes a crucial issue. Previous studies mentioned that the state shift may relates to nonstationarities in electroencephalography (EEG). Therefore, we analyze the nonstationarities of EEG and propose an indicator, the number of stationary components, which is decomposed by a novel algorithm, Stationary Subspace Analysis (SSA), to monitor the human state drift. Moreover, to understand the full spectrum of stationary dynamics changing with brain state, we assessed two datasets: one is lane-keeping task (LKT) dataset, the other is sleep dataset. In LKT dataset, the behavioral results showed that human in alert state has more stationary components than that in drowsy state. Significant negative correlation between driving reaction time (RT) and the number of stationary components was found. Furthermore, significant negative correlation between beta band power of both stationary components and non-stationary components and RT was found. In sleep dataset, human in wakefulness state has more stationary components than that in N1, N2, and N3 stage. Nevertheless, the number of stationary components rose in REM stage. Nonetheless, there is a significant negative correlation between the number of stationary components and the sleep stage before first N3 stage occurred. Apart from that, significant positive correlation was found after people enter deep sleep until waking up. In conclusion, the number of stationary components can be utilized to detect the brain state drift. Such findings could largely reduce the rate of traffic incidents caused by brain state drift.