Heading for new shores! Overcoming pitfalls in BCI design

Chavarriaga, Ricardo; Fried-Oken, Melanie; Kleih, Sonja C.; Lotte, Fabien; Scherer, Reinhold

doi:10.1080/2326263x.2016.1263916

Cited by 75 publications

(66 citation statements)

References 119 publications

(129 reference statements)

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“…NF studies often report a significant proportion of subjects (usually about 30%) that are not able to self-regulate their brain activity (Alkoby et al, 2018). In the brain-computer-interface (BCI) community this phenomenon is known as BCI-deficiency and might originate from non optimal features, flaws from the design (Lotte et al, 2013;Chavarriaga et al, 2017), but also from anatomo-physiological factors that would make some subjects less responsive to certain modalities (Zich et al, 2015). EEG is the most popular NF modality for it is portable, non-invasive and benefits from millisecond temporal resolution.However, its spatial resolution is limited by volume conduction of the head and the number of electrodes.Also, source localization from EEG is inaccurate because of the ill-posed inverse problem (Grech et al, 2008;Baillet et al, 2001).…”

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confidence: 99%

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Perronnet

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Mano

et al. 2018

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Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain function by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly EEG or fMRI. EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new NF approach that consists of providing a NF based simultaneously on EEG and fMRI. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a spectrum of possibilities for defining bimodal NF targets that could be more ro-bust, flexible and effective than unimodal ones. However facing a greater amount of information, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose that the EEG and fMRI features should be represented in a single bimodal feedback, which we refer to as integrated feedback. We then introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The first group (BI DIM, n=10) was shown a two-dimensional (2D) feedback in which each dimension de-picted the information from one modality. The second group (UNI DIM, n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UNI DIM group than in the BI DIM group, which suggests that the 1D feedback is easier to control than the 2D feedback. However when looking at posthoc activation levels, the difference of fMRI activation levels between the NF runs and the preliminary motor imagery run without NF was more significant in the 2D group. Moreover, subjects from the BI DIM group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobule (BI DIM > UNI DIM, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more spe-cific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in combination in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms making the most of the bimodal information and better suited to clinical applications.

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Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Perronnet

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Mano

et al. 2018

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“…The learning task should include an intuitive feedback so that the users can easily understand the action to be executed and improve their performance. However, it is currently hard to choose the right feedback presentation, and it should be a motivating and engaging environment [11], besides being natural and realistic. Here, VR can be shown as a real alternative for tackling the feedback presentation issue.…”

Section: Related Workmentioning

confidence: 99%

“…Despite BCI being a promising and useful application, there are still several challenges to be addressed. Chavarriaga et al [11] discuss concrete research avenues and guidelines to overcome common pitfalls in BCI. Their paper is the outcome of a meeting held at the workshop “What’s wrong with us?…”

Section: Introductionmentioning

confidence: 99%

What if I had a third arm? An EEG study of a supernumerary BCI system

Maciel

et al. 2019

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Motor imagery Brain-Computer Interface (MI-BCI) enables bodyless communication by means of the imagination of body movements. Since its apparition, MI-BCI has been widely used in applications such as guiding a robotic prosthesis, or the navigation in games and virtual reality (VR) environments. Although psychological experiments, such as the Rubber Hand Illusion -RHI, suggest the human ability for creating body transfer illusions, MI-BCI only uses the imagination of real body parts as neurofeedback training and control commands. The present work studies and explores the inclusion of an imaginary third arm as a part of the control commands for MI-BCI systems. It also compares the effectiveness of using the conventional arrows and fixation cross as training step (Graz condition) against realistic human hands performing the corresponding tasks from a first-person perspective (Hands condition); both conditions wearing a VR headset. Ten healthy subjects participated in a two-session EEG experiment involving open-close hand tasks, including a third arm that comes out from the chest. The EEG analysis shows a strong power decrease in the sensory-motor areas for the third arm task in both training conditions. Such activity is significantly stronger for Hands than Graz condition, suggesting that the realistic scenario can reduce the abstractness of the third arm and improve the generation of motor imagery signals. The cognitive load is also assessed both by NASA-TLX and Task Load index.

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“…Both review work in which a BCI aids recovery from disabling conditions, sometimes producing long-term effects that persist after BCI use is discontinued. The third workshop summary considers the barriers to commercial BCI translation and how they can be overcome [8]. …”

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confidence: 99%