Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI

Meng, Jianjun; He, Bin

doi:10.3389/fnhum.2019.00128

Cited by 42 publications

(46 citation statements)

References 79 publications

(103 reference statements)

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“…Moreover, the mean value of

decreases over the runs, which suggests that the synchronization mechanism can be evaluated as the training sessions increases in number. Overall, these outcomes in Figure 8 agree to the results in [ 52 ], evidencing the difficulty of quantifying a significant change in ERD/ERS across the training sessions, even for either channel C3 or C4.…”

Section: Resultssupporting

confidence: 83%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

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“…Moreover, the mean value of

Section: Resultssupporting

confidence: 83%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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“…For instance, Lee et al (2019) found that of all first-time users, 55.6% did not meet the proficiency threshold of 70% during the first session. Similarly, in the study of Meng and He (2019), 18 out of 42 subjects could not reach 70% performance across three training sessions. In another study, Jeunet et al (2016a) identified 16.7% of MI-BCI novice users as inefficient because they did not reach the proficiency threshold on performance.…”

Section: Introductionmentioning

confidence: 83%

“…In another study, Jeunet et al (2016a) identified 16.7% of MI-BCI novice users as inefficient because they did not reach the proficiency threshold on performance. While studies have shown that only one hour of BCI training could already induce structural changes in neural plasticity (Nierhaus et al, 2019), and that especially inefficient users could benefit from more training (Meng and He, 2019), the fact remains that some users do not exceed 70% accuracy (Meng and He, 2019). Despite the reported magnitudes in these studies, the factors and neural mechanisms that underlie the inefficiency phenomenon still remain poorly understood, leading to criticism of the concept of "BCI illiteracy, " which suggests that the inability to control the BCI lies with the person (Thompson, 2019).…”

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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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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“…Therefore, an appropriate training strategy is required to achieve MI-BCI control. Meng et al presented results that showed MI-BCI control accuracy could be significantly increased even after only three MI-BCI training sessions [88]. Simple motor imaging training, without real-time EEG decoding, has also been found to help improve the control accuracy of poor BCI subjects [89].…”

Section: Novel Training Strategymentioning

confidence: 99%

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Zhang

et al. 2020

Brain Science Advances

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Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.

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Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI

Cited by 42 publications

References 79 publications

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

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

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

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