Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN

Xu, Fangzhou; Rong, Fenqi; Leng, Jiancai; Sun, Tao; Zhang, Yang; Siddharth, Siddharth; Jung, Tzyy-Ping

doi:10.1109/tnsre.2021.3123969

Cited by 20 publications

(7 citation statements)

References 23 publications

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“…Furthermore, WD increases complexity, and ED with several attributes could be imprecise [ 101 ]. Second, the number of artificially augmented data samples has a remarkable effect on the performance of the classifier after data augmentation [ 39 ]. It was noted that after a specific number of generated data samples, there is a drastic variation in the model performance either positively or negatively.…”

Section: Discussionmentioning

confidence: 99%

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Generative adversarial networks in EEG analysis: an overview

Habashi

Azab

Eldawlatly

et al. 2023

J NeuroEngineering Rehabil

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Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.

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Section: Discussionmentioning

confidence: 99%

“…In [ 39 ], CycleGAN was used to generate MI data for stroke patients where EEG data was converted to EEG-topography images that had both spatial and spectral features of the EEG. The study adopted S-transform to effectively evaluate ERD/ERS of the EEG, in turn, they could classify different types of MI tasks.…”

Section: Gans For Eeg Tasksmentioning

confidence: 99%

Generative adversarial networks in EEG analysis: an overview

Habashi

Azab

Eldawlatly

et al. 2023

J NeuroEngineering Rehabil

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Section: Single Mode Sensing Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

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Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

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“…MI is regarded as a mental process involving a variety of advanced cognitive functions (Li et al, 2019 ). The MI-based brain-computer interface (BCI) has been widely used in motor function rehabilitation, motor skill learning, and other fields (Long et al, 2011 ; Mane et al, 2020 ; Xu et al, 2021b ). Patients with motor cortex damage can get better functional recovery by MI therapy (Xu et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation

Wang

et al. 2022

Front. Aging Neurosci.

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Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.

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Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN

Cited by 20 publications

References 23 publications

Generative adversarial networks in EEG analysis: an overview

Generative adversarial networks in EEG analysis: an overview

Multimodal Sensing in Stroke Motor Rehabilitation

Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation

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