Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Lee, Hyeon Kyu; Choi, Young-Seok

doi:10.3390/e21121199

Cited by 78 publications

(36 citation statements)

References 31 publications

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“…The average results of this paper show 4-5% improvement over other articles that use the same data set. It's worth noting that in [60], Lee et al converted the CWT to convert EEG into 93 × 32 feature map, and then combined with the advantages of CNN in image processing to obtain a high classification accuracy (92.9%). The method in [60] is enlightening, but the time complexity of the model is higher.…”

Section: Analysis Of Classification Results Of Eeg Signalsmentioning

confidence: 99%

“…It's worth noting that in [60], Lee et al converted the CWT to convert EEG into 93 × 32 feature map, and then combined with the advantages of CNN in image processing to obtain a high classification accuracy (92.9%). The method in [60] is enlightening, but the time complexity of the model is higher. From the point of view of characteristic engineering, this paper extracts time domain feature of EEG signals, whose time complexity is O(1).…”

Section: Analysis Of Classification Results Of Eeg Signalsmentioning

confidence: 99%

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EEG signal classification based on SVM with improved squirrel search algorithm

Shi

Wang

et al. 2020

Biomedical Engineering / Biomedizinische Technik

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Electroencephalography (EEG) is a complex bioelectrical signal. Analysis of which can provide researchers with useful physiological information. In order to recognize and classify EEG signals, a pattern recognition method for optimizing the support vector machine (SVM) by using improved squirrel search algorithm (ISSA) is proposed. The EEG signal is preprocessed, with its time domain features being extracted and directed to the SVM as feature vectors for classification and identification. In this paper, the method of good point set is used to initialize the population position, chaos and reverse learning mechanism are introduced into the algorithm. The performance test of the improved squirrel algorithm (ISSA) is carried out by using the benchmark function. As can be seen from the statistical analysis of the results, the exploration ability and convergence speed of the algorithm are improved. This is then used to optimize SVM parameters. ISSA-SVM model is established and built for classification of EEG signals, compared with other common SVM parameter optimization models. For data sets, the average classification accuracy of this method is 85.9%. This result is an improvement of 2–5% over the comparison method.

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Section: Analysis Of Classification Results Of Eeg Signalsmentioning

confidence: 99%

Section: Analysis Of Classification Results Of Eeg Signalsmentioning

confidence: 99%

EEG signal classification based on SVM with improved squirrel search algorithm

Shi

Wang

et al. 2020

Biomedical Engineering / Biomedizinische Technik

View full text Add to dashboard Cite

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“…On the other hand, deep learning models can generate data-driven features that may be computationally expensive to obtain using other available methods. While many data-driven spatial filtering methods are available [17, 90], identifying frequencies with relevant spectral power often requires either a brute force search or applying techniques such as wavelet convolution to compute power at several frequencies, increasing the size of an already high-dimensional dataset [91, 92]. In contrast, HTNet converges quickly and provides a low-dimensional feature representation in the spectral domain.…”

Section: Discussionmentioning

confidence: 99%

Generalized neural decoders for transfer learning across participants and recording modalities

Peterson

Steine-Hanson

Davis

et al. 2020

Preprint

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Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features. By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

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“…Those factors pose a great challenge for decoding MI-based EEG signals effectively. Researchers have proposed numerous algorithms to process MI signals [ 11 , 12 ], including wavelet transform model [ 13 ], empirical mode decomposition [ 14 ], and common spatial pattern (CSP) [ 15 ]. Among them, CSP is the most popular method to extract features associated with different MI tasks [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding

et al. 2020

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Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject’s intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

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Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Cited by 78 publications

References 31 publications

EEG signal classification based on SVM with improved squirrel search algorithm

EEG signal classification based on SVM with improved squirrel search algorithm

Generalized neural decoders for transfer learning across participants and recording modalities

A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding

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