Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

Kwon, Min Seong; H, Cho; Won, Kyungho; Ahn, Minkyu; Jun, Sung Chan

doi:10.3390/electronics9040690

Cited by 17 publications

(15 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the results of correlation analysis indicate that alpha band RP over SMA from a few seconds before MI was positively correlated with the BCI performance (see Figure 4). Similar results were obtained in previous studies (Ahn et al, 2013;Kwon et al, 2020). In addition, the alpha-band RP from the resting state was proposed before as an index of tracking cognitive function.…”

Section: Discussionsupporting

confidence: 89%

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

Zhou¹,

Lin

Yao

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

show abstract

Section: Discussionsupporting

confidence: 89%

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

Zhou¹,

Lin

Yao

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The crucial relationship with MI-BCI performance was not found using band power and questionnaires. Some studies have reported that SMR such as mu rhythms in the resting-state is related to MI performance ( Blankertz et al, 2010 ; Kwon et al, 2020 ). In fact, this argument seems plausible because alpha and beta power are decreased during the MI and used as typical features of the MI paradigm.…”

Section: Discussionmentioning

confidence: 99%

Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling

Lee

Yoon

Lee

2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection ( r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.

show abstract

“…In an attempt to anticipate the evoked MI responses, several pre-training electrophysiological indicators are reported, like functional connectivity of resting-state networks [ 20 ],

rhythm activity of eyes-open and eyes-closed resting-states [ 21 ], pre-cue EEG rhythms over different brain regions [ 22 ], and the power spectral density estimates of resting wakefulness (before the cue-onset of the conventional MI trial timing and resting state) [ 23 , 24 ]. Although this last predictor is one of the most used, its curve-fitting method depends heavily on various parameters that are difficult to determine, regardless of the resting data employed [ 25 ]. Other predictors are derived from measuring the change in electrophysiological properties across the training sessions [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance

Cited by 17 publications

References 37 publications

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Contact Info

Product

Resources

About