{"title":"A solution to supervised motor imagery task in the BCI Controlled Robot Contest in World Robot Contest","authors":"Huixing Gou, Yi Piao, Jiecheng Ren, Qian Zhao, Yijun Chen, Chang Liu, Wei Hong, Xiaochu Zhang","doi":"10.26599/BSA.2022.9050014","DOIUrl":null,"url":null,"abstract":"Background: One of the most prestigious competitions in the world is the World Robot Conference. This paper presents the winning solution to the supervised motor imagery (MI) task in the BCI Controlled Robot Contest in World Robot Contest 2021. Methods: Data augmentation, preprocessing, feature extraction, and model training are the main components of the solution. The model is based on EEGNet, a popular convolutional neural networks model for classifying electroencephalography data. Results: Despite the model’s lack of stability, this solution was the most successful in the task. The channels closest to the vertex were the most helpful in feature extraction. Conclusion: This solution is suitable for supervised MI tasks not only in this competition but also in future scenarios.","PeriodicalId":67062,"journal":{"name":"Brain Science Advances","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Science Advances","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.26599/BSA.2022.9050014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Background: One of the most prestigious competitions in the world is the World Robot Conference. This paper presents the winning solution to the supervised motor imagery (MI) task in the BCI Controlled Robot Contest in World Robot Contest 2021. Methods: Data augmentation, preprocessing, feature extraction, and model training are the main components of the solution. The model is based on EEGNet, a popular convolutional neural networks model for classifying electroencephalography data. Results: Despite the model’s lack of stability, this solution was the most successful in the task. The channels closest to the vertex were the most helpful in feature extraction. Conclusion: This solution is suitable for supervised MI tasks not only in this competition but also in future scenarios.
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