Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Wu, Huanyu; Wu, Dongrui

doi:10.26599/bsa.2022.9050001

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The more commonly used BCI experimental paradigms today are BCI-based event-related potentials (ERP), BCI-based steady-state visual evoked potentials (SSVEP) and BCI-based motor imagery (MI-BCI). In recent years, more and more novel paradigms based on the previous paradigms have also been discovered, such as the rapid sequence visual presentation (RSVP) paradigm [6] and the radial contraction-expansion paradigm [7] . Among the above BCI paradigms, MI-BCI has attracted higher attention.…”

Section: Introductionmentioning

confidence: 99%

Motor imagery classification of finger movements based on position subtask network learning

Luo¹,

Ye²,

Lu³

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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The multi-classification of finger movements is a difficult challenge at present. In this paper, a multi-task Siamese network is proposed. Based on the basic Siamese network architecture, this study designs the position relationships between sample pairs and constructs single-sample subtasks for the left and right input samples. The position relationships between samples not only additionally supplement the spatial factors between electroencephalogram (EEG) signals that facilitate the generalization performance of position network learning, but also turn the EEG signals set structured so that the multi-task Siamese network is used to effectively learn multiple relationships between samples. During the model testing phase, a balanced matching algorithm of single test sample VS multi-training sample is used to improve the robustness of the multi-task Siamese network model. The experimental results on the 5F motor imagery data set from The Largest SCP of Motor-Imagery, an EEG data signal repository, and data set4 of the 4th brain-computer interface (BCI) competition show that compared with the single-subtask Siamese network, EEGNet, ResNet-50 and the public experimental results of the original dataset, the multi-task Siamese network in this paper can obtain better multiclass motor imagery (MI) classification accuracy and can better realize the multi-classification of MI-EEG signals. This study could be helpful to promote the development of finger-based MI-BCI system.

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Section: Introductionmentioning

confidence: 99%

Motor imagery classification of finger movements based on position subtask network learning

Luo¹,

Ye²,

Lu³

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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“…These features are then used to train a logistic regression classifier. The results revealed that Wu’s method considerably improved the classification performance of RSVP across subjects [22]. Furthermore, combining conventional spatial filtering estimation with deep learning methods improved performance.…”

Section: Introductionmentioning

confidence: 99%

A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022

Wang,

Zhang,

et al. 2023

Brain Science Advances

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The rapid serial visual presentation (RSVP) paradigm has garnered considerable attention in brain–computer interface (BCI) systems. Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models. In this study, we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022. We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection. The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks. We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.

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CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional Autoencoder for Dual-Target RSVP-BCI Tasks

Chen,

Wang,

et al. 2024

IEEE Trans. Biomed. Eng.

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Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Cited by 5 publications

References 28 publications

Motor imagery classification of finger movements based on position subtask network learning

Motor imagery classification of finger movements based on position subtask network learning

A review of deep learning methods for cross-subject rapid serial visual presentation detection in World Robot Contest 2022

CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional Autoencoder for Dual-Target RSVP-BCI Tasks

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