Outline

  • 1 Introduction
  • 2 Evaluation Methods
  • 2.1 Behavioral Studies
  • 2.2 Inquiries
  • 2.3 Physiological Sensors
  • 2.4 Neuroimaging
  • 2.5 a New Continuum for Hci Evaluation Methods
  • 3 Constructs
  • 3.1 Workload
  • 3.1.1 Definition
  • 3.1.2 Neuroimaging
  • 3.2 Attention – Vigilance – Fatigue
  • 3.2.1 Definition
  • 3.2.2 Neuroimaging
  • 3.3 Error Recognition
  • 3.3.1 Definition
  • 3.3.2 Neuroimaging
  • 3.4 Emotions
  • 3.4.1 Definition
  • 3.4.2 Neuroimaging
  • 3.5 Engagement – Flow – Immersion
  • 3.5.1 Definition
  • 3.5.2 Neuroimaging
  • 4 Challenges
  • 4.1 Improving on Constructs
  • 4.2 Assessing New Constructs
  • 4.2.1 Usability – Comfort
  • 4.2.2 User Experience
  • 4.3 Hardware – Signal Processing
  • 5 Conclusion

رئوس مطالب

  • چکیده
  • کلیدواژه ها
  • 1.مقدمه
  • 2. روش‌های ارزیابی
  • 2.1. مطالعات رفتاری
  • 2.2. پرس و جو
  • 2.3. حسگرهای فیزیولوژیکی
  • 2.4. تصویربرداری عصبی
  • 2.5. پیوستار جدید برای روش‌های ارزیابی کنش متقابل انسان و رایانه
  • 3. سازه‌ها
  • 3.1. حجم کار
  • 3.1.1. تعریف
  • 3.1.2. تصویربرداری عصبی
  • 3.2. توجه- هشیاری- خستگی
  • 3.2.1. تعریف
  • 3.2.2. تصویربرداری عصبی
  • 3.3. تشخیص خطا
  • 3.3.1. تعریف
  • 3.3.2. تصویربرداری عصبی
  • 3.4. هیجانات
  • 3.4.1. تعریف
  • 3.4.2. تصویربرداری عصبی
  • 3.5. تعهد- جریان- غوطه‌وری
  • 3.5.1. تعریف
  • 3.5.2. تصویربرداری عصبی
  • 4. چالش‌ها
  • 4.1. اصلاح سازه‌ها
  • 4.2. ارزیابی سازه‌های جدید
  • 4.2.1. قابلیت استفاده- راحتی
  • 4.2.2. تجربه کاربر
  • 4.3. پردازش سخت‌افزار- پردازش
  • 5. نتیجه‌گیری

Abstract

Evaluating human-computer interaction is essential as a broadening population uses machines, sometimes in sensitive contexts. However, traditional evaluation methods may fail to combine real-time measures, an “objective” approach and data contextualization. In this review we look at how adding neuroimaging techniques can respond to such needs. We focus on electroencephalography (EEG), as it could be handled effectively during a dedicated evaluation phase. We identify workload, attention, vigilance, fatigue, error recognition, emotions, engagement, flow and immersion as being recognizable by EEG. We find that workload, attention and emotions assessments would benefit the most from EEG. Moreover, we advocate to study further error recognition through neuroimaging to enhance usability and increase user experience.

Keywords: - - - - -

Conclusions

We reviewed how neuroimaging techniques could assess constructs relevant for HCI evaluation. Between the four categories of evaluation methods, inquiries could deliver more qualitative data, while physiological sensors and neuroimaging are exocentric measures (the most “objective” measures of subjectively perceived stimuli). It is particularly interesting to combine those methods for constructs otherwise difficult to assess with exactitude, as investigated in many studies (Ravaja, 2009), (Nacke and Lindley, 2009), (van Erp et al., 2010), (Chanel et al., 2011).

Our analysis of neuroimaging techniques focused on EEG as it promises a good trade-off between cost, time resolution and ease of installation. We advocate that neurotechnologies can bring useful insights to HCI evaluation. EEG devices are not yet perfectly reliable and practical to use; hardware and software processing are still evolving. However their cumbersomeness is partially avoided if they are used during a dedicated evaluation phase in the HCI development process, with specially enrolled users (testers).

We studied workload, attention, vigilance, fatigue, error recognition, emotions, engagement, flow and immersion. Figure 2 stimulates thoughts about their relationships with HCI components. Some constructs should benefit more than the others from EEG measures: 1) workload, EEG being more sensible to changes compared to other methods (Mathan et al., 2007); 2) attention, because event related potentials could help to anticipate how many details users register (Mustafa et al., 2012); 3) emotions, with an arousal/valence state measured over a short timeframe (Chanel et al., 2011). Error recognition could hardly be assessed precisely with anything but neuroimaging. Such construct highlights how innovative this evaluation method is. Among the outlined challenges, a continuous and modulated error recognition would greatly help to assess usability and comfort.

Next studies should start to combine the various constructs, along with a comprehensive framework which gathers every evaluation method, one’s advantages preventing others’ drawbacks. This should lead to an increase of the overall user experience.

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