EEG Generation of Virtual Channels Using an Improved Wasserstein Generative Adversarial Networks

Li, Ling-Long; Cao, Guangzhong; Liang, Hongjie; Chen, Jiang-Cheng; Zhang, Yuepeng

doi:10.1007/978-3-031-13841-6_36

Cited by 5 publications

(8 citation statements)

References 19 publications

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“…Visualizing the reconstructed data and the real data, we observed that at most points the reconstructed data were close to the real values. However, when several bad channels are close to each other, the interpolation algorithm fits poorly at points with higher amplitudes, and a similar phenomenon was observed in the Bahador et al (2021) and Li et al (2022) experiments. This part of the data points causes a large reconstruction error.…”

Section: Discussionsupporting

confidence: 69%

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Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

Liu,

Wang,

Qiu

et al. 2023

Front. Neurosci.

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During the acquisition of electroencephalographic (EEG) signals, various factors can influence the data and lead to the presence of one or multiple bad channels. Bad channel interpolation is the use of good channels data to reconstruct bad channel, thereby maintaining the original dimensions of the data for subsequent analysis tasks. The mainstream interpolation algorithm assigns weights to channels based on the physical distance of the electrodes and does not take into account the effect of physiological factors on the EEG signal. The algorithm proposed in this study utilizes an attention mechanism to allocate channel weights (AMACW). The model gets the correlation among channels by learning from good channel data. Interpolation assigns weights based on learned correlations without the need for electrode location information, solving the difficulty that traditional methods cannot interpolate bad channels at unknown locations. To avoid an overly concentrated weight distribution of the model when generating data, we designed the channel masking (CM). This method spreads attention and allows the model to utilize data from multiple channels. We evaluate the reconstruction performance of the model using EEG data with 1 to 5 bad channels. With EEGLAB’s interpolation method as a performance reference, tests have shown that the AMACW models can effectively reconstruct bad channels.

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Section: Discussionsupporting

confidence: 69%

“… Li et al (2022) designed the GAN (IWGAN) for EEG channel data generation using WGAN as a framework. WGAN is more stable than the original GAN ( Arjovsky et al, 2017 ), and IWGAN further improves the training stability by improving the loss function.…”

Section: Related Researchmentioning

confidence: 99%

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

Liu,

Wang,

Qiu

et al. 2023

Front. Neurosci.

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“…For instance, Corley and Huang (2019) implemented super-resolution (SR) based on generative adversarial networks (GANs) to decrease EEG device channel number requirements. Li et al (2022) proposed an improved Wasserstein generative adaptive network (WGAN) for generating EEG samples in virtual channels, improving sample action classification accuracy. Svantesson et al (2021) demonstrated that using neural networks to restore or upsample EEG signals was a viable alternative to spherical spline interpolation.…”

Section: Introductionmentioning

confidence: 99%

Design of virtual BCI channels based on informer

Sun

Zhang

2023

Front. Hum. Neurosci.

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The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers’ workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer’s features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life.

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“…With the rapid development of science and technology in recent decades, extensive research has been conducted on brain activity. Many researchers have studied the brain from the perspectives of magnetoencephalography (MEG) [2] and functional magnetic resonance imaging [3]; however, EEG dominates the field. Developing a wearable, noninvasive EEG acquisition device is a promising research area.…”

Section: Introductionmentioning

confidence: 99%

“…Super-resolution (SR) can be considered a method of signal amplification, designed to acquire more comprehensive information from limited input data. Li et al [2] designed Wasserstein generative adversarial networks (WGANs) to obtain virtual acquisition channels by using the existing EEG channel and improve the classification accuracy. Corley et al [16] upsampled the EEG channel by using generative adversarial networks (GANs).…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Level Separation: A Method for Enhancing Electroencephalogram Classification Accuracy Through Super-Resolution Level Separation

Sun,

Li,

Zhang

2024

IEEE Access

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Accurate identification of electroencephalogram (EEG) signals forms the basis for the development and application of brain-computer interface (BCI) devices. Signal preprocessing is an essential part of most EEG classification and recognition systems.In this study, we proposed a method called Superresolution level separation (SRLS) to add dimensions of information to the classification model. First, we calculated the correlation between the EEG signals acquired by each channel and divided these channels into multiple levels. Next, we used a super-resolution method to calculate common EEGs (C-EEGs) acquired by the channels. In addition, we designed an EEG-split-informer (ES-informer) model based on an informer model to enable small-sample users to obtain highly fitting C-EEGs. We then calculated the difference between the C-EEG and true EEG (T-EEG) to obtain the unique EEGs (U-EEGs) acquired by each channel, thus, adding dimensions to the data inputted to the classification model. Utilizing the 2008 2a motor imagery (MI) EEG dataset from the BCI Competition and the P300 paradigm data collected, EEGNet was employed as the classification model to validate the efficacy of the proposed SRLS method. The results of the experiments indicated that the SRLS method augmented the input dimension of the model and amplified the classification accuracy by over 7% for MI and 3.6% for P300. These findings demonstrate that SRLS is capable of enhancing the recognition accuracy of EEG.

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EEG Generation of Virtual Channels Using an Improved Wasserstein Generative Adversarial Networks

Cited by 5 publications

References 19 publications

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm

Design of virtual BCI channels based on informer

Super-Resolution Level Separation: A Method for Enhancing Electroencephalogram Classification Accuracy Through Super-Resolution Level Separation

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