Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external dev...Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external devices.There are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and classification.In traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data.The major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy results.In this study,motor imagery(MI)signals have been classified using different deep learning algorithms.We have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset IIa.The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms.Amongst the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.展开更多
基金This research was financially supported in part by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program.(Project No.P0016038)in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Brain-Computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware directly.The recorded brain data is converted directly to the machine that can be used to control external devices.There are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and classification.In traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery data.The major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy results.In this study,motor imagery(MI)signals have been classified using different deep learning algorithms.We have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset IIa.The outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning algorithms.Amongst the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.
