Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates

Vasilyev, Anatoly; Liburkina, Sofya; Yakovlev, Lev; Perepelkina, O. V.; Kaplan, Arline

doi:10.1016/j.neuropsychologia.2017.02.005

Cited by 83 publications

(65 citation statements)

References 55 publications

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“…Around 15-30% users are inherently unable to produce taskspecific signature robust enough to control a BCI (Blankertz et al, 2009;Vidaurre and Blankertz, 2010). The underlying causes of this BCI illiteracy are not well-understood; however, diverse psychological and neurophysiological predictors appear to be associated with BCI performance (Blankertz et al, 2009;Vidaurre and Blankertz, 2010;Jensen et al, 2011;Hammer et al, 2012;Ahn and Jun, 2015;Jeunet et al, 2015;Reichert et al, 2015;Zhang et al, 2015;Acqualagna et al, 2016;Vasilyev et al, 2017;Sannelli et al, 2019).…”

Section: Bci Performance Predictorsmentioning

confidence: 99%

“…Cognitive and neurological factors including functions and anatomy along with emotional and mental processes give rise to intra-and inter-subject variability affecting the performance of SMR-based BCI (Wens et al, 2014;Reichert et al, 2015;Zhang et al, 2015;Acqualagna et al, 2016;Betzel and Bassett, 2017;Vasilyev et al, 2017;Seghier and Price, 2018;Betzel et al, 2019;Smith et al, 2019). Time-variant cognitive factors such as fatigue, memory load, attention and reaction time modulate instantaneous brain activity, and can cause inconsistent SMRbased BCI performance (Hammer et al, 2012;Ahn and Jun, 2015;Fox et al, 2015;Jeunet et al, 2015;Darvishi et al, 2018;Sannelli et al, 2019).…”

Section: Bci Performance Predictorsmentioning

confidence: 99%

“…RSNs represent large-scale spatiotemporal structures exhibiting intrinsic brain activities that are thought to be functionally relevant (Deco et al, 2011). Studies have shown intra-and inter-subject variability in sensorimotor RSN, which may have implications for BCI performance variability (Jensen et al, 2011;Wens et al, 2014;Reichert et al, 2015;Zhang et al, 2015;Acqualagna et al, 2016;Vasilyev et al, 2017). It has been hypothesized that SMR-based BCI performance predictor is reliable for people who display strong resting EEG amplitudes (Blankertz et al, 2010;Suk et al, 2014;Sannelli et al, 2019).…”

Section: Bci Performance Predictorsmentioning

confidence: 99%

“…Figure 1 schematically illustrates the idea of covariate shift when the training and test data distributions are different. The underlying time-variant and subject-specific brain dynamics depends on associated psychological and neurophysiological factors (Blankertz et al, 2009;Vidaurre and Blankertz, 2010;Jensen et al, 2011;Hammer et al, 2012;Ahn and Jun, 2015;Jeunet et al, 2015;Reichert et al, 2015;Zhang et al, 2015;Acqualagna et al, 2016;Vasilyev et al, 2017;Sannelli et al, 2019) and cause covariate shift in EEG-derived feature distributions (Krusienski et al, 2011;Fazli et al, 2015;Jayaram et al, 2016).…”

Section: Covariate Shift and Transfer Learningmentioning

confidence: 99%

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Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Saha

Baumert

2020

Front. Comput. Neurosci.

176

109

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Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra-and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

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Section: Bci Performance Predictorsmentioning

confidence: 99%

Section: Bci Performance Predictorsmentioning

confidence: 99%

Section: Bci Performance Predictorsmentioning

confidence: 99%

Section: Covariate Shift and Transfer Learningmentioning

confidence: 99%

See 2 more Smart Citations

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Saha

Baumert

2020

Front. Comput. Neurosci.

176

109

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“…, which involves imagination of the movement of different parts of the body, resulting in sensorimotor cortex activity, detected as event-related EEG oscillations and transformed then into the signal sent to computer. BCI based on MI technology require repetitive training and can achieve quite high accuracy after several sessions (Prakaksita et al, 2016;Vasilyev et al, 2017;Škola et al, 2019). [(Vourvopoulos and i Badia, 2016)] 3.…”

Section: Motor Imagery (Mi)mentioning

confidence: 99%

Playing a P300-based BCI VR game leads to changes in cognitive functions of healthy adults

Bulat

Karpman²,

Samokhina³

et al. 2020

Preprint

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In this paper, we present the results of a study to determine the effect of the P300-based brain-computer interface (BCI) virtual reality game on the cognitive functions of healthy human subjects. This study is a part of on-going research related to evaluation of the long-term effect of P300 training in Virtual Reality surrounding (VR game) on the cognitive performance of the young healthy population.A comparison of results between 3 groups of participants (15 people each) revealed the progressing difference in cognitive assessment for experimental group played P300-BCI VR game, showing the positive increase in flanker and conjunction visual search task performance associated with selective attention and mental inhibition. We show that the effect is due to the use of P300 BCI paradigm. Our results suggest that P300 BCI games combined with virtual reality can not only be used for rehabilitation in patients with slight mental disorders or elderly, but for increasing some cognitive functions in healthy subjects, giving an additional improvement in learning in case of combination with possible educational tasks or used for attention training. K E Y W O R D S

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BCI-Controlled Motor Imagery Training Can Improve Performance in e-Sports

Yakovlev

Syrov

Görtz

et al. 2020

Communications in Computer and Information Science

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Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates

Cited by 83 publications

References 55 publications

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Playing a P300-based BCI VR game leads to changes in cognitive functions of healthy adults

BCI-Controlled Motor Imagery Training Can Improve Performance in e-Sports

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