執行雙重任務駕駛行為其專注力干擾與腦波變化之關聯

Abstract

現今日常生活中感官刺激數量不斷增加，然而受限於有限的認知資源/能力，當需執行多重刺激的過程中便會選擇性地優先處理部份感官信息/刺激。但相較於處理單一個信息/刺激，同時執行多個信息/刺激則通常會對注意力造成干擾 (attentional interferences)。感官訊息的增加對於駕車安全是首當其衝，開車分心便是起因於同時處理多個刺激/事件而駕駛人轉移其注意力到非開車任務上，而開車分心對用路人所造成之生命財產危害益加嚴重更導致許多災難性的後果。因此制定一對策用以追蹤/監測駕駛人之注意力是勢在必行。本研究招募十位健康的受測者參加了雙重任務駕駛行為實驗，此雙重任務駕駛行為實驗中包括一個車道保持任務和一數學心算任務，並於此雙重任務實驗中加入400 ms目標物時距探討不同分心程度，而在執行此一雙重任務駕駛實驗期間，受測者之行為反應（behavior）與腦電圖（EEG）資料會同步收錄。接著應用藉著獨立成份分析 (independent component analysis, ICA)為一空間濾波器，用以從多個電極所記錄之腦電圖數據變化中找出腦部獨立訊號源。執行雙重任務期間，從行為資料發現到會因注意力受到干擾而降低受測者之行為表現，另一方面，觀察執行雙重任務情況下所誘發的腦動態變化，在同一腦區之下會與個別單一任務所誘發之腦部活動變化有一關聯。而基於此雙重任務下腦波現象的發現，我們設計以支持向量機為基礎的注意力估測系統，運用處理單一任務時之腦波動態當作訓練資料並進一步評估受測者執行雙重任務時的專注力變化。透過腦波的變化該系統在辨識受測者其專注力於執行數學或是開車之正確率分別為84.6±5.8％與86.2±5.4％。透過行為表現與透過腦波判斷之專注力變化，受測者會策略性的調控其認知能力/資源借以在有限的認知資源下盡快完成處理兩事件。透過本研究之發現，在執行多重任務之駕車確實對於駕駛人之行為與腦波造成影響，也進一步驗證此一基於腦部動態變化之專注力變化估測系統之可行性。

In our daily life, we selectively attend to an overload of sensory information with our limited cognitive resources. Compared to deal with only one task individually, there is usually an attentional interference as performing multiple cognitive demands simultaneously. In particular, distracted driving leads to many catastrophic consequences. Developing a countermeasure to track drivers’ focus of attention (FOA) and engagement of operators in dual (multi)–tasking conditions is thus imperative. Ten healthy volunteers participated in a dual-task experiment that comprised two cognitive tasks: a lane-keeping driving task and a mathematical problem-solving task during which their behaviors and electroencephalogram (EEG) were concurrently recorded. The stimulus onset asynchrony (SOA) effect with 400 ms latency was considered to induce different levels of distraction. Independent Component Analysis (ICA) was employed as a spatial filter to separate the contributions of independent sources from the recorded EEG data. During dual task, the attentional interferences should cause of behavioral impairments, and the measured brain dynamics composed the activation elicited by each single task. According to these physiological findings, one focus of attention (FOA) assessment system based on support vector machine (SVM) was built to evaluate participants’ FOAs during dual-task conditions. This system achieved 84.6±5.8% and 86.2±5.4% classification accuracies in detecting the participants’ FOAs on the math vs. driving tasks, respectively. The detected FOAs revealed that participants’ cognitive attention and strategies dynamically changed between tasks to optimize the overall performance, as attention was limited and competed. The empirical results of this study demonstrate the feasibility of a practical system to continuously estimating attention through EEG spectra.