Experimenters' Influence on Mental-Imagery based Brain-Computer Interface User Training

Pillette, Léa; Roc, Aline; N’Kaoua, Bernard; Lotte, Fabien

doi:10.1016/j.ijhcs.2021.102603

Cited by 41 publications

(48 citation statements)

References 46 publications

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“…In addition, differences in gender performances might have been prevailing because all experimenters in the current experiment were females. While Roc et al (2019) suggested that female experimenters might positively influence subjects' performance, Pillette et al (2021), using the same data, reported that the evolution of MI-BCI performance is dependent on the interaction between experimenters' and subjects' gender as well as subjects' tension level and that it cannot be firmly said that female experimenters benefit all subjects. This is while Wood and Kober (2018) found that female participants trained by female experimenters performed significantly worse than those trained by male experimenters.…”

Section: Fundamental Characteristicsmentioning

confidence: 98%

“…Fundamental user characteristics were reported by Randolph (2012), who showed that females are likely to be better performers than males. Additionally, the effect of gender is shown to be modulated by that of the experimenter (Wood and Kober, 2018;Roc et al, 2019;Pillette et al, 2021). Other fundamental characteristics include age, which was reported to influence performance (Randolph et al, 2010) and the dominant hand of the user (Zapała et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

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Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users’ mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.

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Section: Fundamental Characteristicsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

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“…For the exploratory phase, we used the standard "Graz BCI" bar feedback (Pfurtscheller and Neuper, 2001), as implemented in OpenViBE for 2-class MT-BCIs and as used in, e.g., Roc et al, 2019;Pillette et al, 2021. In that phase each run included 20 trials for each of the two MT classes. A classifier was built on the data from the two first runs to provide online feedback for the subsequent training runs of that session.…”

Section: Bci Protocolmentioning

confidence: 99%

Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training

Benaroch

Sadatnejad

Roc

et al. 2021

Front. Hum. Neurosci.

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While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition.

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“…Also, the influence of the participants' tension on MI-BCI performances seems to be modulated by the gender of the experimenter, which can be considered as a complex type of social presence and emotional feedback. Tensed and non tensed participants had better performances when training with men experimenters and women experimenters, respectively [33].…”

Section: Influence Of the Participants' Profile On Bci User Trainingmentioning

confidence: 97%

Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

Pillette

N’Kaoua

Sabau

et al. 2021

MTI

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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.

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Experimenters' Influence on Mental-Imagery based Brain-Computer Interface User Training

Cited by 41 publications

References 46 publications

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training

Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

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