EMG-Based Cross-Subject Silent Speech Recognition Using Conditional Domain Adversarial Network

Zhang, Yakun; Cai, Huihui; Wu, Jinghan; Xie, Liang; Xu, Minpeng; Ming, Dong; Yan, Ye; Yin, Erwei

doi:10.1109/tcds.2023.3316701

Cited by 1 publication

(1 citation statement)

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“…The concept behind ADANN is to extract a universal feature representation from this multidomain setting. Zhang et al (Y. Zhang et al, 2023) proposed an improved conditional domain adversarial network (ICDAN). This model calculates the conditional domain adversarial network (CDAN) loss between source domain features and target domain features through a discriminator.…”

Section: Adversarial Learningmentioning

confidence: 99%

Robust myoelectric pattern recognition methods for reducing users’ calibration burden: challenges and future

Wang,

Ao,

2024

Front. Bioeng. Biotechnol.

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Myoelectric pattern recognition (MPR) has evolved into a sophisticated technology widely employed in controlling myoelectric interface (MI) devices like prosthetic and orthotic robots. Current MIs not only enable multi-degree-of-freedom control of prosthetic limbs but also demonstrate substantial potential in consumer electronics. However, the non-stationary random characteristics of myoelectric signals poses challenges, leading to performance degradation in practical scenarios such as electrode shifting and switching new users. Conventional MIs often necessitate meticulous calibration, imposing a significant burden on users. To address user frustration during the calibration process, researchers have focused on identifying MPR methods that alleviate this burden. This article categorizes common scenarios that incur calibration burdens as based on data distribution shift and based on dynamic data categories. Then further investigated and summarized the popular robust MPR algorithms used to reduce the user’s calibration burden. We categorize these algorithms as based on data manipulate, feature manipulation and, model structure. And describes the scenarios to which each method is applicable and the conditions required for calibration. Finally, this review is concluded with the advantages of robust MPR and the remaining challenges and future opportunities.

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Section: Adversarial Learningmentioning

confidence: 99%