Abstract
By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback.
Highlights
Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to send commands to a digital tool, for example, a wheelchair or a video-game, by performing motor-imagery tasks only, for example, imagining hand movements, while their brain activity is recorded [1]
We focused on traits and states that were shown to have an influence on motor imagery (MI)-BCI performances in previous studies, that is, mental rotation scores (MRS) [29], tension and autonomy traits [31]
We verified if there were significant differences of mental rotation scores (MRS), tension, autonomy or initial kinaesthetic or visual imagery abilities in the groups depending on the modality of feedback that they started training with, that is, “Order”
Summary
Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to send commands to a digital tool, for example, a wheelchair or a video-game, by performing motor-imagery tasks only, for example, imagining hand movements, while their brain activity is recorded [1]. They can be used for neurofeedback training [2]. The aim is to promote neurological modifications and thereby motor recovery [3] Despite their very promising applications, BCIs are not much developed outside research laboratories yet. When the system has to decide which task the user is performing between two motor imagery tasks, for example, imagining a right versus a left hand movement, on average the system is mistaken once every four predictions [4]
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