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Lee M, Park HY, Park W, Kim KT, Kim YH, Jeong JH. Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1767-1778. [PMID: 38683717 DOI: 10.1109/tnsre.2024.3395133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.
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Yan S, Hu Y, Zhang R, Qi D, Hu Y, Yao D, Shi L, Zhang L. Multilayer network-based channel selection for motor imagery brain-computer interface. J Neural Eng 2024; 21:016029. [PMID: 38295419 DOI: 10.1088/1741-2552/ad2496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/31/2024] [Indexed: 02/02/2024]
Abstract
Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.
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Affiliation(s)
- Shaoting Yan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yuxia Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Rui Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Daowei Qi
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Yubo Hu
- The No.3 Provincial People's Hospital of Henan Province, Zhengzhou, People's Republic of China
| | - Dezhong Yao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, People's Republic of China
- Beijing National Research Center for Information Science and Technology, Beijing, People's Republic of China
| | - Lipeng Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
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Guo D, Hu J, Wang D, Wang C, Yue S, Xu F, Zhang Y. Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography. Front Neurosci 2024; 18:1330280. [PMID: 38370433 PMCID: PMC10869475 DOI: 10.3389/fnins.2024.1330280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/16/2024] [Indexed: 02/20/2024] Open
Abstract
Objective The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets. Methods Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared. Results PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area. Conclusion Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.
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Affiliation(s)
- Dongju Guo
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Jinglu Hu
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Dezheng Wang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Shouwei Yue
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yang Zhang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, Shandong, China
- Rehabilitation and Physical Therapy Department, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, Shandong, China
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Lee M, Kim YH, Lee SW. Motor Impairment in Stroke Patients is Associated with Network Properties During Consecutive Motor Imagery. IEEE Trans Biomed Eng 2022; 69:2604-2615. [PMID: 35171761 DOI: 10.1109/tbme.2022.3151742] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Our study aimed to predict the Fugl-Meyer assessment (FMA) upper limb using network properties during motor imagery using electroencephalography (EEG) signals. METHODS The subjects performed a finger tapping imagery task according to consecutive cues. We measured the weighted phase lag index (wPLI) as functional connectivity and directed transfer function (DTF) as causal connectivity in healthy controls and stroke patients. The network properties based on the wPLI and DTF were calculated. We predicted the FMA upper limb using partial least squares regression. RESULTS A higher DTF in the mu band was observed in stroke patients than in healthy controls. Notably, the difference in local properties at node F3 was negatively correlated with motor impairment in stroke patients. Finally, using significant network properties based on the wPLI and DTF, we predicted motor impairments using the FMA upper limb with a root-mean-square error of 1.68 (R2 = 0.97). This outperformed the state-of-the-art predictors. CONCLUSION These findings demonstrate that network properties based on functional and causal connectivity were highly associated with motor function in stroke patients. SIGNIFICANCE Our network properties can help calculate the predictor of motor impairments in stroke rehabilitation and provide insight into the neural correlates related to motor function based on EEG after reorganization induced by stroke.
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