A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

Fleury, Mathis; Lioi, Giulia; Barillot, Christian; Lécuyer, Anatole

doi:10.3389/fnins.2020.00528

Cited by 55 publications

(47 citation statements)

References 111 publications

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“…Numerous research studies have been led to assess the efficiency of providing feedback through other modalities than the visual one, i.e. auditory feedback [72], proprioceptive feedback [189,285] or vibrotactile feedback [55,172,247].…”

Section: Feedback Modalitymentioning

confidence: 99%

A review of user training methods in brain computer interfaces based on mental tasks

et al. 2021

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Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training-notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.

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Section: Feedback Modalitymentioning

confidence: 99%

A review of user training methods in brain computer interfaces based on mental tasks

et al. 2021

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“…Regarding the type of feedback provided to the user, in other words, the different modalities provided by the BCI system (visual, auditory, and haptic [ 50 ]) from which the subject perceives sensations, it was shown that eight of the proposed works was based exclusively on visual feedback, six on combined visual and auditory feedback, two on haptic and visual feedback, and two implemented visual, auditory, and haptic feedback.…”

Section: Resultsmentioning

confidence: 99%

Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review

Camargo-Vargas

Callejas-Cuervo

Mazzoleni

2021

Sensors

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In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.

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“…Compared to robotic devices, vibrotactile interfaces with proprioceptive feedback functions are more comfortable, affordable, portable, cheaper and easier to implement. However, up until now, vibrotactile feedback has been scarcely explored in BCI-based NF training applications (for a recent review see [43]). The pioneering studies on this type of haptic feedback were conducted by Chatterjee et al [44] and Cincotti et al [17].…”

Section: Introductionmentioning

confidence: 99%

A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery

Grigorev

Savosenkov

Lukoyanov

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MIthe event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI. Index Terms-Brain-computer interfaces (BCI), vibrotactile feedback, motor imagery (MI), event-related desynchronization (ERD), motor cortical excitability, navigated transcranial magnetic stimulation (nTMS), motor evoked potentials (MEPs).

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A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

Cited by 55 publications

References 111 publications

A review of user training methods in brain computer interfaces based on mental tasks

A review of user training methods in brain computer interfaces based on mental tasks

Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review

A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery

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