A Study in User-Centered Design and Evaluation of Mental Tasks for BCI

Bos, Danny Plass-Oude; Poel, Mannes; Nijholt, Anton

doi:10.1007/978-3-642-17829-0_12

Cited by 24 publications

(18 citation statements)

References 13 publications

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“…These inputs suffer from problems related to noise, non-stationarities, and ambiguity [5]. This can be problematic, as input is the basis for usable systems in general, and recognition accuracy is most important to users of BCIs [6].…”

Section: Introductionmentioning

confidence: 99%

Perception and Manipulation of Game Control

Bos

Laar

Reuderink

et al. 2014

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Brain-computer interfaces do not provide perfect recognition of user input, for similar reasons as natural input modalities. How well can users assess the amount of control they have, and how much control do they need? We describe an experiment where we manipulated the control users had in a keyboard-controlled browser game. The data of 211 runs from 87 individuals indicates a significant linear correlation between users' sense of control and the amount of control they really had in terms of mutual information (not accuracy!). If users know what they put in, they can assess quite well how much control they have over the system. In our case, from an amount of control of above 0.68 bits in mutual information (a 5-class accuracy of 65%), this aspect of control no longer seems to be the critical factor for finishing the game. Deliberate manipulation of perception may offer a way to make imperfect, uncertain input modalities more acceptable, especially in combination with games.

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Section: Introductionmentioning

confidence: 99%

Perception and Manipulation of Game Control

Bos

Laar

Reuderink

et al. 2014

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Self Cite

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“…In the context of BCI applications, the user-centred approach has already been introduced (e.g., Kübler et al, 2013, Schreuder et al, 2013. However, its use is more recent for the design of BCI videogames (e.g., Plass-Oude Bos et al, 2011;Van de Laar et al, 2011). This approach remains minor compared to the technocentric approach.…”

Section: Introductionmentioning

confidence: 99%

User-Centred BCI Videogame Design

Loup-Escande

Lotte

Loup

et al. 2015

Handbook of Digital Games and Entertainment Technologies

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This chapter aims to offer a user-centred methodological framework to guide the design and evaluation of Brain-Computer Interface videogames. This framework is based on the contributions of ergonomics to ensure these games are well suited for their users (i.e., players).It provides methods, criteria and metrics to complete the different phases required by ae human-centred design process. This aims to understand the context of use, specify the user needs and evaluate the solutions in order to define design choices. Several ergonomic methods (e.g., interviews, longitudinal studies, user based testing), objective metrics (e.g., task success, number of errors) and subjective metrics (e.g., mark assigned to an item) are suggested to define and measure the usefulness, usability, acceptability, hedonic qualities, appealingness, emotions related to user experience, immersion and presence to be respected. The benefits and contributions of the user centred framework for the ergonomic design of these Brain-Computer Interface Videogames are discussed.2

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“…Consequently, there is a great need to drive the MI tasks used in these non-invasive BCIs towards user-centered applications such that realistic goals can be realized [4,5]. The aforementioned MI tasks are irrelevant to many rehabilitative and recreational uses of BCIs and other tasks must be investigated that better represent the functionality of the application.…”

Section: Introductionmentioning

confidence: 99%

Discriminating hand gesture motor imagery tasks using cortical current density estimation

Edelman

Baxter

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Current EEG based brain computer interface (BCI) systems have achieved successful control in up to 3 dimensions; however the current paradigm may be unnatural for many rehabilitative and recreational applications. Therefore there is a great need to find motor imagination (MI) tasks that are realistic for output device control. In this paper we present our results on classifying hand gesture MI tasks, including right hand flexion, extension, supination and pronation using a novel EEG inverse imaging approach. By using both temporal and spatial specificity in the source domain we were able to separate MI tasks with up to 95% accuracy for binary classification of any two tasks compared to a maximum of only 79% in the sensor domain.

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A Study in User-Centered Design and Evaluation of Mental Tasks for BCI

Cited by 24 publications

References 13 publications

Perception and Manipulation of Game Control

Perception and Manipulation of Game Control

User-Centred BCI Videogame Design

Discriminating hand gesture motor imagery tasks using cortical current density estimation

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