Freeing the visual channel by exploiting vibrotactile BCI feedback

Leeb, Robert; Gwak, Kiuk; Kim, Dae‐Shik; Millán, José del R.

doi:10.1109/embc.2013.6610195

Cited by 31 publications

(35 citation statements)

References 11 publications

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“…Yet, both theoretical [5] and practical [20] evidences argue for the use of other sensori-modalities that could be more adapted to BCI-based applications. Among these modalities, the tactile channel seems to be a good candidate as it is often not overtaxed in interaction contexts, contrary to the visual and auditory ones.…”

Section: B Multimodalitymentioning

confidence: 96%

Towards improved BCI based on human learning principles

Lotte

Jeunet

2015

The 3rd International Winter Conference on Brain-Computer Interface

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Although EEG-based BCI are very promising for numerous applications, they mostly remain prototypes not used outside laboratories, due to their low reliability. Poor BCI performances are partly due to imperfect EEG signal processing algorithms but also to the user, who may not be able to produce reliable EEG patterns. This paper presents some of our current work that aims at addressing the latter, i.e., at guiding users to learn BCI control mastery. First, this paper identifies some theoretical (based on human learning psychology models) and practical limitations of current standard BCI training approaches and thus the need for alternative ones. To try to address these limitations, we conducted a study to explore what kind of users can use a BCI and why, and will present the main results. We also present new feedback types we designed to help users to learn BCI control skills more efficiently.

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Section: B Multimodalitymentioning

confidence: 96%

Towards improved BCI based on human learning principles

Lotte

Jeunet

2015

The 3rd International Winter Conference on Brain-Computer Interface

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“…First, auditory feedback has been used for BCI training (Hinterberger et al, 2004;Gargiulo et al, 2012;McCraedie et al, 2014) and has been experimentally proved to be as effective as visual feedback despite being slower (Nijboer et al, 2008). Second, tactile feedback has also been tested and shown to be comparable to visual feedback for motor-imagery based BCIs (Kauhanen et al, 2006;Cincotti et al, 2007;Chatterjee et al, 2007;Leeb et al, 2013). In some cases, it has even been shown to be better: when proprioceptive feedback was provided using a robotic arm (Gomez-Rodriguez et al, 2011) or when vibrations were provided on the hands, during hand motorimagery, using vibrotactile motors (Jeunet et al, 2015).…”

Section: Adapting the Feedbackmentioning

confidence: 99%

Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review

et al. 2018

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In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain-Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.'s review by reporting recent additions to the BCI literature. The object of this paper is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another. By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance-related factors. Since BCIs and NF share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomized controlled trials.

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“…(2) All these feedbacks involve the visual channel, which is often overtaxed in interaction situations. Nevertheless, using a visual feedback independent from the interactive application (e.g., the game or navigation task) would force the user to split his attention between different visual information (the game and the MI-BCI feedback), thus demanding more cognitive resources [15].…”

Section: Visual Feedback For Mi-bcismentioning

confidence: 99%

“…Yet, this visual feedback is difficult to assimilate when integrated with the visual layout of the primary interactive application that it supports [9]. Indeed, the visual channel is often overtaxed in interactive environments [15]. Thus, integrating the visual feedback into the application increases the number of visual search tasks.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, tactile feedback, although popular in other areas of HCI, has not received much attention for MI-BCI despite its advantages such as: (a) freeing the visual channel in order to reduce cognitive workload [15], (b) maintaining a certain amount of privacy, as it is more difficult to be perceived by the surroundings than the visual or auditory ones, and (c) the possibility to be used in a wide range of interactive tasks, such as in gaming conditions. Using tactile feedback will separate the application channel (visual) from the MI-BCI feedback channel (tactile), thus potentially improving the branching condition of the application.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context

Jeunet

Thanh

Spelmezan

et al. 2015

Lecture Notes in Computer Science

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Motor-Imagery based Brain Computer Interfaces (MI-BCIs) allow users to interact with computers by imagining limb movements. MI-BCIs are very promising for a wide range of applications as they offer a new and nontime locked modality of control. However, most MI-BCIs involve visual feedback to inform the user about the system's decisions, which makes them difficult to use when integrated with visual interactive tasks. This paper presents our design and evaluation of a tactile feedback glove for MI-BCIs, which provides a continuously updated tactile feedback. We first determined the best parameters for this tactile feedback and then tested it in a multitasking environment: at the same time users were performing the MI tasks, they were asked to count distracters. Our results suggest that, as compared to an equivalent visual feedback, the use of tactile feedback leads to a higher recognition accuracy of the MI-BCI tasks and fewer errors in counting distracters.

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Freeing the visual channel by exploiting vibrotactile BCI feedback

Cited by 31 publications

References 11 publications

Towards improved BCI based on human learning principles

Towards improved BCI based on human learning principles

Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review

Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context

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