A novel motor imagery EEG decoding method based on feature separation

Yang, Lie; Song, Yujiang; Ma, Ke; Su, Enze; Xie, Longhan

doi:10.1088/1741-2552/abe39b

Cited by 16 publications

(13 citation statements)

References 40 publications

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“…They were usually focused on the methods to improve the prediction accuracy [5,8,23,[30][31][32], raise the number of commands [12,33], increase the information transfer rate (ITR) [34][35][36][37][38], or reduce the training efforts [7,30,34,39]. To enhance the prediction accuracy, new classification algorithms [30,40,41] or feature extraction methods have been proposed [31,32,42]. Recent BCI studies frequently applied deep learning algorithms for high accuracy [5,23].…”

Section: Discussionmentioning

confidence: 99%

Studies to Overcome Brain–Computer Interface Challenges

Choi

Yeom

2022

Applied Sciences

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A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system.

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Section: Discussionmentioning

confidence: 99%

Studies to Overcome Brain–Computer Interface Challenges

Choi

Yeom

2022

Applied Sciences

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“…BP in [8,30] Hz CSP Particle swarm optimization and extreme learning machine BCIc.IV-2a (B), BCIc.III-3a (B) [136] Channel selection, FB in [8,32] Hz and in [6,30] Hz BCIc.IV-2a (M) [121] convolutional layers were mostly exploited. Recurrent neural networks like the long short-term memory [121] and adversarial-based methods [31] were used as well. In the multi-class cases, the mean classification accuracy of most performing deep neural networks resulted equal to 85% with 4% uncertainty, while non-deep artificial neural networks did not result among the most performing ones.…”

Section: Csp Random Forestmentioning

confidence: 99%

“…In general, it should be noted that BI approaches are much more effective in multiclass classification than non-BI approaches. Typically, for deep approaches the raw EEG signal [31,32] or 2D or 3D arrays obtained by Fourier or wavelet transforms [33,107,122] were given as input. With a few exceptions [121,123], features were extracted by hand mainly for the other methods.…”

Section: Csp Random Forestmentioning

confidence: 99%

“…This is indeed true for signal acquisition, where the intention is to ensure a reliable, affordable, wearable, and portable end-user device [11]. Nevertheless, much research considers the development of EEG processing strategies for MI-BCI with the aim of improving the classification performance [31][32][33]. A crucial point for a robust and reliable system consists of achieving high classification performance indeed.…”

Section: Introductionmentioning

confidence: 99%

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How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

et al. 2022

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Objective. Processing strategies are analysed with respect to the classification of electroencephalographic signals related to brain-computer interfaces based on motor imagery. A review of literature is carried out to understand the achievements in motor imagery classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility. Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery- based brain-computer interfaces. Article search was carried out in accordance with the PRISMA standard and 89 studies were included. Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85 % to 100 % range for the binary case and in the 83 % to 93 % range for multi-class one. Associated uncertainties are up to 6 % while repeatability for a predetermined dataset is up to 8 %. Reproducibility assessment was instead prevented by lack of standardization in experiments. Significance. By relying on the analysed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a brain-computer interface. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of results reproducibility.

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“…WithoutLossNet proves the effectiveness of the central loss function on the BCIIV2a and SMR-BCI datasets, and the central loss function can well compensate for the shortcomings of Softmax. The combination of the central loss function and Softmax classifier can take into account the relationship between the class and different classes at the same time, so that the same type of data converges to the feature center, and the distance between the feature centers between different classes expands [33,34]. SingleScaleNet replaces different scale convolution kernels with 3x3 convolution kernels, which verifies that the features extracted from different scales of sensory fields are diverse, and the feature representations learned by multi-scale are more comprehensive and diverse.…”

Section: Ablation Experiments Analysismentioning

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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A novel motor imagery EEG decoding method based on feature separation

Cited by 16 publications

References 40 publications

Studies to Overcome Brain–Computer Interface Challenges

Studies to Overcome Brain–Computer Interface Challenges

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

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

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