Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study

Zhang, Feng; Sun, Yi; Qian, Linze; Qi, Yu; Wang, Yueming; Guan, Cuntai; Sun, Yu

doi:10.1109/tbme.2021.3115799

Cited by 21 publications

(7 citation statements)

References 66 publications

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“…A large body of work on detecting movement intentions for the upper limbs based on EEG signals has led to the development of BCI systems [30][31][32][33]. Some works [34,35] even use visual stimuli based on SSVEP EEG to detect the motor intention of the subjects.…”

Section: Related Workmentioning

confidence: 99%

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Asanza

Peláez

Loayza

et al. 2022

Sensors

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Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Asanza

Peláez

Loayza

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…We conduct extensive experiments to verify the effectiveness of the proposed method. Source codes for experiments are publicly available 1 .…”

Section: Methodsmentioning

confidence: 99%

Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces

Huang

Liang

Zhang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subjectspecific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects.

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“…Moreover, all the datasets in method comparison are classbalance. Thus, in the following these literatures [16], [62], [63], we randomly select 70% data from the target domain set and combine it with the source domain set for training, while the remaining data in the target domain set are used to validate the proposed method. Note that the labeling information of the target domain data is not involved in training.…”

Section: Comparative Studiesmentioning

confidence: 99%

Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network

Liu

Zhang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electroencephalogram (EEG) classification has attracted great attention in recent years, and many models have been presented for this task. Nevertheless, EEG data vary from subject to subject, which may lead to the performance of a classifier degrades due to individual differences. To collect enough labeled data to model would address the issue, but it is often time-consuming and laborintensive. In this paper, we propose a new multi-source transfer learning method based on domain adversarial neural network for EEG classification. Specifically, we design a domain adversarial neural network, which includes a feature extractor, a classifier, and a domain discriminator, and therefore reduce the domain shift to achieve the purpose. In addition, a unified multi-source optimization framework is constructed to further improve the performance, and the result for EEG classification is induced by the weighted combination of the predictions from multiple source domains. Experiments on two publicly available EEG datasets validate the advantages of the proposed method.

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Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study

Cited by 21 publications

References 66 publications

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces

Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network

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