The influence of motivation and emotion on sensorimotor rhythm‐based brain–computer interface performance

Kleih, Sonja C.; Botrel, Loïc; Kübler, Andrea

doi:10.1111/psyp.13832

Cited by 9 publications

(12 citation statements)

References 65 publications

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“…Last but not least, several studies point to the improvement of feedback and training methods through immersive and engaging environments, such as robotic platforms (Alimardani et al, 2018), virtual reality (Coogan and He, 2018), and gamification (de Castro-Cros et al, 2020). This resonates well with past research on user motivation and its effect on BCI performance (Nijboer et al, 2010;Alimardani et al, 2014;Sannelli et al, 2019;Kleih-Dahms et al, 2021). Therefore, employing state-of-the-art classification methods as well as training/feedback design is a promising avenue for future research to reduce the prevalence and severity of the BCI inefficiency problem.…”

Section: Future Researchsupporting

confidence: 54%

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis

Yoon

Alimardani

2021

Front. Hum. Neurosci.

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Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.

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Section: Future Researchsupporting

confidence: 54%

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis

Yoon

Alimardani

2021

Front. Hum. Neurosci.

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“…The level of motivation and mood may impact the participants’ engagement with the NF training ( Kleih-Dahms et al, 2011 ; Ros et al, 2020 ). Recently NF studies have established the importance of motivational value as an essential addition in predicting the success of NF training ( Diaz Hernandez et al, 2018 ; Perez-Elvira et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Source localized infraslow neurofeedback training in people with chronic painful knee osteoarthritis: A randomized, double-blind, sham-controlled feasibility clinical trial

et al. 2022

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Persistent pain is a key symptom in people living with knee osteoarthritis (KOA). Infra-slow Neurofeedback (ISF-NF) training is a recent development focusing on modulating cortical slow-wave activity to improve pain outcomes. A parallel, two-armed double-blinded, randomized sham-controlled, feasibility clinical trial aimed to determine the feasibility and safety of a novel electroencephalography-based infraslow fluctuation neurofeedback (EEG ISF-NF) training in people with KOA and determine the variability of clinical outcomes and EEG changes following NF training. Eligible participants attended nine 30-min ISF-NF training sessions involving three cortical regions linked to pain. Feasibility measures were monitored during the trial period. Pain and functional outcomes were measured at baseline, post-intervention, and follow-up after 2 weeks. Resting-state EEG was recorded at baseline and immediate post-intervention. Participants were middle-aged (61.7 ± 7.6 years), New Zealand European (90.5%), and mostly females (62%) with an average knee pain duration of 4 ± 3.4 years. The study achieved a retention rate of 91%, with 20/22 participants completing all the sessions. Participants rated high levels of acceptance and “moderate to high levels of perceived effectiveness of the training.” No serious adverse events were reported during the trial. Mean difference (95% CI) for clinical pain and function measures are as follows for pain severity [active: 0.89 ± 1.7 (−0.27 to 2.0); sham: 0.98 ± 1.1 (0.22–1.7)], pain interference [active: 0.75 ± 2.3 (−0.82 to 2.3); Sham: 0.89 ± 2.1 (−0.60 to 2.4)], pain unpleasantness [active: 2.6 ± 3.7 (0.17–5.1); sham: 2.8 ± 3 (0.62–5.0)] and physical function [active: 6.2 ± 13 (−2.6 to 15); sham: 1.6 ± 12 (−6.8 to 10)]. EEG sources demonstrated frequency-specific neuronal activity, functional connectivity, and ISF ratio changes following NF training. The findings of the study indicated that the ISF-NF training is a feasible, safe, and acceptable intervention for pain management in people with KOA, with high levels of perceived effectiveness. The study also reports the variability in clinical, brain activity, and connectivity changes following training.

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“…Consequently, a number of the existing previous studies investigated whether influencing or controlling for the user's motivation and affective state could intentionally enhance BCI performance in either the P300-BCI or the SMR-BCI. Kleih-Dahms et al (2021) for example manipulated the user's mood by experimental mood induction via video clips or music. Examination of the performance of healthy users included SMR-BCI-based motor imagery-induced movement and action control.…”

Section: Current Motivation and Mood Statesmentioning

confidence: 99%

Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user – previous evidence, self-relevance and the user’s self-concept as potential superordinate human factors of relevance

Herbert

2024

Front. Hum. Neurosci.

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Brain–computer interfaces (BCIs) are well-known instances of how technology can convert a user’s brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user’s internal mental states and traits including motivation, affect or cognition, personality traits, or the user’s satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user’s self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user’s self-concept (self-referential knowledge and beliefs about one’s self) guide information processing and modulate the user’s motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one’s self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user’s self-concept in the BCI setting – including paradigms, user instructions, and training sessions.

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The influence of motivation and emotion on sensorimotor rhythm‐based brain–computer interface performance

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 9 publications

References 65 publications

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Source localized infraslow neurofeedback training in people with chronic painful knee osteoarthritis: A randomized, double-blind, sham-controlled feasibility clinical trial

Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user – previous evidence, self-relevance and the user’s self-concept as potential superordinate human factors of relevance

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