OBJECTIVE: In recent years, brain-computer interfaces (BCIs) have become mature enough to immensely benefit from the expertise and tools established in the field of human-computer interaction (HCI). One of the core objectives in HCI research is the design of systems that provide a pleasurable user experience (UX). While the majority of BCI studies exclusively evaluate common efficiency measures such as classification accuracy and speed, single research groups have begun to look at further usability aspects such as ease of use, workload and learnability. However, these evaluation metrics only cover pragmatic aspects of UX while still not considering the hedonic quality of UX. In order to gain a holistic perspective on UX, hedonic quality aspects such as motivation and frustration were also taken into account for our evaluation of three BCI-driven interfaces, which were proposed to be used as a two-stage neuroprosthetic control within the EU project MUNDUS. APPROACH: At the first stage, one of six possible actions was selected and either confirmed or cancelled at the second stage. For the experiment, a solely event-related-potential-based interface (ERP-ERP) and two hybrid solutions were tested that were controlled by ERP and motor imagery (MI)--resulting in the two possible combinations: ERP selection/MI confirmation (ERP-MI) or MI selection/ERP confirmation (MI-ERP). Behavioural, subjective and encephalographic (EEG) data of 12 healthy subjects were collected during an online experiment with the three graphical user interfaces (GUIs). MAIN RESULTS: Results showed a significantly greater pragmatic quality (in terms of accuracy, efficiency, workload, use quality and learnability) for the ERP-ERP and ERP-MI GUIs in contrast to the MI-ERP GUI. Consequently, the MI-ERP GUI is least suited for use as a neuroprosthetic control. With respect to the comparison of the ERP-ERP and ERP-MI GUIs, no significant differences in pragmatic and hedonic quality of UX were found. Since throughout better results were obtained for the conventional approach and it was most preferred by the subjects, the ERP-ERP GUI seems more suitable for its deployment in actual end-users. Nevertheless, for individuals with stable MI patterns, the hybrid interface can be provided as an additional option of choice within the MUNDUS framework. SIGNIFICANCE: Although the paramount goal in BCI research still remains the improvement of classification accuracy and communication speed, it is of significance to note that it is equally important for end-users to keep up their motivation and prevent frustration. By including pragmatic as well as hedonic quality aspects, this study is the first effort to gain a holistic perspective of the UX while interacting with BCI-driven assistive technology aimed at actual end-users. The broad-scale methodology provided valuable insights into the underlying dynamics causing the users' experience to differ across the GUIs. The results will be used to refine a BCI-driven neuroprosthesis and test it with end-users.
OBJECTIVE: In recent years, brain-computer interfaces (BCIs) have become mature enough to immensely benefit from the expertise and tools established in the field of human-computer interaction (HCI). One of the core objectives in HCI research is the design of systems that provide a pleasurable user experience (UX). While the majority of BCI studies exclusively evaluate common efficiency measures such as classification accuracy and speed, single research groups have begun to look at further usability aspects such as ease of use, workload and learnability. However, these evaluation metrics only cover pragmatic aspects of UX while still not considering the hedonic quality of UX. In order to gain a holistic perspective on UX, hedonic quality aspects such as motivation and frustration were also taken into account for our evaluation of three BCI-driven interfaces, which were proposed to be used as a two-stage neuroprosthetic control within the EU project MUNDUS. APPROACH: At the first stage, one of six possible actions was selected and either confirmed or cancelled at the second stage. For the experiment, a solely event-related-potential-based interface (ERP-ERP) and two hybrid solutions were tested that were controlled by ERP and motor imagery (MI)--resulting in the two possible combinations: ERP selection/MI confirmation (ERP-MI) or MI selection/ERP confirmation (MI-ERP). Behavioural, subjective and encephalographic (EEG) data of 12 healthy subjects were collected during an online experiment with the three graphical user interfaces (GUIs). MAIN RESULTS: Results showed a significantly greater pragmatic quality (in terms of accuracy, efficiency, workload, use quality and learnability) for the ERP-ERP and ERP-MI GUIs in contrast to the MI-ERP GUI. Consequently, the MI-ERP GUI is least suited for use as a neuroprosthetic control. With respect to the comparison of the ERP-ERP and ERP-MI GUIs, no significant differences in pragmatic and hedonic quality of UX were found. Since throughout better results were obtained for the conventional approach and it was most preferred by the subjects, the ERP-ERP GUI seems more suitable for its deployment in actual end-users. Nevertheless, for individuals with stable MI patterns, the hybrid interface can be provided as an additional option of choice within the MUNDUS framework. SIGNIFICANCE: Although the paramount goal in BCI research still remains the improvement of classification accuracy and communication speed, it is of significance to note that it is equally important for end-users to keep up their motivation and prevent frustration. By including pragmatic as well as hedonic quality aspects, this study is the first effort to gain a holistic perspective of the UX while interacting with BCI-driven assistive technology aimed at actual end-users. The broad-scale methodology provided valuable insights into the underlying dynamics causing the users' experience to differ across the GUIs. The results will be used to refine a BCI-driven neuroprosthesis and test it with end-users.
Authors: Sam Darvishi; Alireza Gharabaghi; Chadwick B Boulay; Michael C Ridding; Derek Abbott; Mathias Baumert Journal: Front Neurosci Date: 2017-02-09 Impact factor: 4.677