運用腦波分析於人機互動設計：視覺注意力偵測與基於聽知覺的使用性測試

Abstract

認知功能在我們的日常生活中扮演重要的角色,如:聽覺感知,注意力分佈。在現 代科技進步下,越來越多複雜的資訊系統圍繞在我們的生活中,但人類的認知能力是有 限的,因此探索人機互動過程中所造成的認知負荷以及改變使相當重要的議題。 目前在人機互動領域中,通常利用問卷與外在行為評估互動界面有效性的方式。過去的 方法雖然提供了簡易的方式評估使用者界面,但較難了解使用者在與系統互動過程中注 意力或感知變化。 因此,在本論文研究中我們透過整合互動界面與腦波技術,分別探索使用者在互動 過程中的視覺注意力與聽覺感知變化。在視覺注意力部分,我們利用Steady state visual evoked potential (SSVEP) 偵測使用者在視覺記憶任務中,視覺注意力的變化情形。在實 驗中,我們結合眼動儀觀察使用者眼動軌跡與其內在注意力之間的關聯,我們發現使用 者外在的眼動軌跡不見得會與其內在的注意力一致。因此,根據實驗結果,我們利用 SSVEP技術設計偵測視覺注意力的方式,其判斷使用者內在注意力正確率約為 76%~ 81%。在聽覺感知部分,我們利用Mismatch Negativity (MMN) 以及 P3a 腦波,探討現 行手機的訊息提示音對於聽覺感知與注意力的影響。透過此方法,我們可以了解不同提 示音設計對於使用者的潛在影響,並改善現有評估提示音的測試方式,讓提示音有效性 測試能夠更加客觀且自然。 本研究利用腦波技術,分別探討視覺注意力與聽覺感知在人機互動過程中的變化, 並依據實驗結果提出實作以及設計上的建議,我們希望在本研究能提供人機互動研究新 的觀點與方法。

Human cognitive limitations are becoming a crucial issue in human computer interaction nowadays and thus an important determinant of overall human performance. This thesis purpose to introduce two methods which applying EEG technology help us to improve and evaluate human computer interaction. First, we suggested a new approach to detect users’ attention state in chapter 2. Attention monitoring is particularly important for many HCI applications. How to automatically determine a users visual attention state is challenging since attention involves many complex and internal human cognitive functions. Behavioral observations, such as eye gaze or response to external stimuli, can provide some clues for users visual attention state; however, the users cognitive state cannot be easily known. We explored the feasibility of designing an attention monitoring system that can detect if our brain sees a visual stimulus consciously. Second, we used an EEG-based approach to assist usability test with audio notifications in chapter 3. Audio notifications have become an important way to prompt users. Several studies have been proposed to evaluate audio notifications, but they rarely considered user workload and environmental impact in the same time. We developed an EEG-based approach to evaluate audio notifications by measuring subjects’ auditory perceptual response (mismatch negativity) and attention status (P3a). We demonstrated this approach by two experiments, in which auditory icons were evaluated under different workload and environments. According to the experiment results, the perceptual effects of audio notifications could be measured objectively. Based on these technologies, we could directly detect users’ cognitive changing when they interact with our design. Therefore, this thesis used EEG device to detect and analysis variation of human cognitive state from visual and auditory perception respectively. We hope to provide new feasible approaches and view to HCI field.