Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnhum.2020.00321/full |
| _version_ | 1818392239727968256 |
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| author | Minji Lee Jae-Geun Yoon Seong-Whan Lee |
| author_facet | Minji Lee Jae-Geun Yoon Seong-Whan Lee |
| author_sort | Minji Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T05:26:16Z |
| format | Article |
| id | doaj.art-605e67c9d1db498fa9dc9031442fd753 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-14T05:26:16Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-605e67c9d1db498fa9dc9031442fd7532022-12-21T23:15:31ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612020-08-011410.3389/fnhum.2020.00321534889Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal ModelingMinji Lee0Jae-Geun Yoon1Seong-Whan Lee2Department of Brain and Cognitive Engineering, Korea University, Seoul, South KoreaDepartment of Brain and Cognitive Engineering, Korea University, Seoul, South KoreaDepartment of Artificial Intelligence, Korea University, Seoul, South KoreaMotor 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.https://www.frontiersin.org/article/10.3389/fnhum.2020.00321/fullmotor imagerybrain-computer interfacedynamic causal modelingeffective connectivityelectroencephalography |
| spellingShingle | Minji Lee Jae-Geun Yoon Seong-Whan Lee Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling Frontiers in Human Neuroscience motor imagery brain-computer interface dynamic causal modeling effective connectivity electroencephalography |
| title | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
| title_full | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
| title_fullStr | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
| title_full_unstemmed | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
| title_short | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
| title_sort | predicting motor imagery performance from resting state eeg using dynamic causal modeling |
| topic | motor imagery brain-computer interface dynamic causal modeling effective connectivity electroencephalography |
| url | https://www.frontiersin.org/article/10.3389/fnhum.2020.00321/full |
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