Ensemble learning method based on temporal, spatial features with multi-scale filter banks for motor imagery EEG classification

Zheng, Lanhong; Feng, Wei; Ma, Yue; Lian, Pengchen; Yang, Xiao Li; Yi, Zhengkun; Wang, Xinyu

doi:10.1016/j.bspc.2022.103634

Cited by 16 publications

(8 citation statements)

References 37 publications

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“…Studies into offline EEG classification have proposed a variety of novel ensemble learning techniques that could, in the future, be applied to online dynamic device control. Salient ensemble learning approaches applied to offline data have aimed to produce more generalizable classifiers that are robust to the artifacts and nonstationarities present in EEG data [ 88 , 89 , 90 ]. In 2019, Raza et al [ 88 ] presented an adaptive ensemble learning technique.…”

Section: Signal-processing and Classification Techniques At The Cutti...mentioning

confidence: 99%

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A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.

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Section: Signal-processing and Classification Techniques At The Cutti...mentioning

confidence: 99%

“…The proposed approach outperformed various benchmarking techniques. In 2022, Zheng et al [ 89 ] proposed an ensemble learning technique based on temporal features, spatial features, and a multiscale filter bank. First, bootstrap sampling was used to divide the EEG data into subsets.…”

Section: Signal-processing and Classification Techniques At The Cutti...mentioning

confidence: 99%

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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“…Yang et al learned temporal and frequency domain features from EEG data in parallel through a two-branch CNN and used them as input to the MI decoder for classification [12]. Ma et al used shallow dual-branch CNN for MI classification and demonstrated that the use of dual-branch parallel feature extraction was effective in improving the classification accuracy [13].Multi-scale feature extraction aims to learn latent features at different granularities through different receptive fields for EEG signals [16,17,18,19]. Zheng et al utilized the MSFBCNN structure in the feature extraction layer to extract enough latent features to achieve feature diversification [19].…”

Section: Subject-independent Eeg Classification Of Motor Imagery Base...mentioning

confidence: 99%

Subject-independent EEG classification of motor imagery based on a dual-branch feature fusion

Dong

Han

Wen

et al. 2022

Preprint

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Background: Brain-computer interface (BCI) system helps people with motor dysfunction interact with external environment. With the advancement of technology, the BCI system has been applied in practice, but its practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient’s energy and patience and can easily lead to anxiety. We propose a subject-independentzero calibration approach to solve this problem. Methods: A dual-branch multiscale autoencoder network(MSAENet) is proposed realize the subject-independent classification in motor imagery, aiming to realize the plug-and-play of BCI. Firstly, the network consists of a multiscale branch and an autoencoder (AE) for feature learning from different perspectives. Secondly, the covariance between the EEG signal and the common spatial pattern in the 8-30 Hz band was used as spatio-spectral features, and the feature pre-extracted information is used as the input of MSAENet. Finally, The network introduces a central loss function to improve the classification ability. Testing network generalizability on three publicly available datasets BCIIV2a, SMR-BCI, and OpenBMI. Results: The results show that our proposed network shows good results on all three datasets. In the case of subject-independence, MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets,while achieving F1 score values as high as 69. 34% on the OpenBMI dataset.Subject-dependent results with the best classification performance were significantly better than the other four advanced comparison methods. Our method can maintain a better classification accuracy while ensuring a small amount of parameters and a short prediction time. Conclusions: MSAENet verifies the following three points: (1)Spatial frequency domain features can extract effective information from raw EEG signals. (2)Two-branch multiscale feature fusion can extract features more comprehensively. (3)The integration of the central loss function compensates for the fact that Softmax classifiers only consider class spacing and ignore intra-class distance. Our proposed method achieves zero calibration, and the problem that requires a lot of calibration time in BCI applications is effectively solved.Keywords: BCI; Motor imagery; Subject-independent; Feature fusion; Zero Calibration

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“…They used hybrid-scale rather than single-scale temporal filters on the input EEG data to learn the temporal frequency information at different levels. [42] proposed an ensemble learning method based on temporal and spatial features and multi-scale filter banks which called TSMFBEL.…”

Section: Introductionmentioning

confidence: 99%

TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding

Qin,

Zhang,

Tao

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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The electroencephalogram-based (EEG) braincomputer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG's spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.

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Ensemble learning method based on temporal, spatial features with multi-scale filter banks for motor imagery EEG classification

Cited by 16 publications

References 37 publications

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Subject-independent EEG classification of motor imagery based on a dual-branch feature fusion

TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding

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