Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment

Alimardani, Maryam; Nishio, S.; Ishiguro, Hiroshi

doi:10.5772/intechopen.78695

Cited by 46 publications

(43 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nijboer et al (2008) found that mastery confidence increased BCI performance, but fear of incompetence was related to decreased performance. While Hammer et al (2012) could not replicate the same effect, other studies have shown that indirect ways of enhancing motivation such as multi-user BCI games (Bonnet et al, 2013), biased feedback (Alimardani et al, 2014), embodiment of robotic or virtual bodies (Alimardani et al, 2018;Škola et al, 2019;Choi et al, 2020) and immersive technologies (Coogan and He, 2018) can improve users' performance. Task motivation and engagement could also improve performance by inducing internal competition (Perdikis et al, 2018) or through a continuous pursuit task (Edelman et al, 2019).…”

Section: Introductionmentioning

confidence: 88%

“…Future studies investigating the effects of motivation may not only passively measure one's motivation, but also actively induce it. Examples of enhancing motivation include better embodiment by using robotic or virtual bodies (Alimardani et al, 2018;Škola et al, 2019), multi-user BCI games (Bonnet et al, 2013;Daeglau et al, 2020), or feedback that incorporates a positive bias (Alimardani et al, 2014).…”

Section: Temporal Factorsmentioning

confidence: 99%

“…In order to gain better results, the authors advise further research with an updated BCI paradigm to make it a reliable extension of the current state of the art. There are multiple improvements in the field that suggest motor imagery learning among novice BCI users; for example, it has been shown that realistic feedback from humanlike bodies and the feeling of embodiment improves the modulation of brain activity needed for motor imagery (Alimardani et al, 2018;Penaloza et al, 2018;Choi et al, 2020). Furthermore, visual guidance in virtual reality (Liang et al, 2016;Coogan andHe, 2018), gamification (de Castro-Cros et al, 2020), and multimodal visual-haptic feedback (Wang et al, 2019) can improve learning of MI-BCI.…”

Section: Future Researchmentioning

confidence: 99%

See 2 more Smart Citations

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

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 88%

Section: Temporal Factorsmentioning

confidence: 99%

Section: Future Researchmentioning

confidence: 99%

See 1 more Smart Citation

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

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…MI essentially requires numerous sessions of training because many individuals cannot easily conceptualize MI 9 . It is a counterintuitive task involving a broad spectrum of processes ranging from visual imagery (VI) to kinesthetic imagery (KI), first- to third-person perspectives 10,11 , and non-target-oriented to target-oriented movements 12 . In disabled patients who do not produce overt motor output, it can be especially challenging to generate stable and continuous brain signal patterns 13 .…”

Section: Introductionmentioning

confidence: 99%

Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery

Lee

Kim

Seo

et al. 2019

Sci Rep

View full text Add to dashboard Cite

Motor imagery (MI) for target-oriented movements, which is a basis for functional activities of daily living, can be more appropriate than non-target-oriented MI as tasks to promote motor recovery or brain-computer interface (BCI) applications. This study aimed to explore different characteristics of brain activation among target-oriented kinesthetic imagery (KI) and visual imagery (VI) in the first-person (VI-1) and third-person (VI-3) perspectives. Eighteen healthy volunteers were evaluated for MI ability, trained for the three types of target-oriented MIs, and scanned using 3 T functional magnetic resonance imaging (fMRI) under MI and perceptual control conditions, presented in a block design. Post-experimental questionnaires were administered after fMRI. Common brain regions activated during the three types of MI were the left premotor area and inferior parietal lobule, irrespective of the MI modalities or perspectives. Contrast analyses showed significantly increased brain activation only in the contrast of KI versus VI-1 and KI versus VI-3 for considerably extensive brain regions, including the supplementary motor area and insula. Neural activity in the orbitofrontal cortex and cerebellum during VI-1 and KI was significantly correlated with MI ability measured by mental chronometry and a self-reported questionnaire, respectively. These results can provide a basis in developing MI-based protocols for neurorehabilitation to improve motor recovery and BCI training in severely paralyzed individuals.

show abstract

“…One of the key components of BCI training is the feedback given to the subject to improve the learning of an MI task and promote motivation and engagement [9]. Traditionally, most MI-based NF systems have used visual feedback due to the simplicity of its implementation and understanding by the user [10][11][12][13]. However, in some cases, visual feedback is not suitable, for example, for patients with an impaired visual system [14] or under the circumstances of the visual channel overload [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A BCI-based vibrotactile neurofeedback training improves motor cortical excitability during motor imagery

Grigorev

Savosenkov

Lukoyanov

et al. 2021

Preprint

View full text Add to dashboard Cite

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 MI – the 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.

show abstract

Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment

Cited by 46 publications

References 70 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

Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery

A BCI-based vibrotactile neurofeedback training improves motor cortical excitability during motor imagery

Contact Info

Product

Resources

About