{"title":"使用可解释的人工智能加速大脑研究","authors":"Jing-Lun Chou, Ya-Lin Huang, Chia-Ying Hsieh, Jian-Xue Huang, Chunshan Wei","doi":"10.1109/ICMEW56448.2022.9859322","DOIUrl":null,"url":null,"abstract":"In this demo, we present ExBrainable, an open-source application dedicated to modeling, evaluating and visualizing explainable CNN-based models on EEG data for brain/neuroscience research. We have implemented the functions including EEG data loading, model training, evaluation and parameter visualization. The application is also built with a model base including representative convolutional neural network architectures for users to implement without any programming. With its easy-to-use graphic user interface (GUI), it is completely available for investigators of different disciplines with limited resource and limited programming skill. Starting with preprocessed EEG data, users can quickly train the desired model, evaluate the performance, and finally visualize features learned by the model with no pain.","PeriodicalId":106759,"journal":{"name":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Accelerating Brain Research using Explainable Artificial Intelligence\",\"authors\":\"Jing-Lun Chou, Ya-Lin Huang, Chia-Ying Hsieh, Jian-Xue Huang, Chunshan Wei\",\"doi\":\"10.1109/ICMEW56448.2022.9859322\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this demo, we present ExBrainable, an open-source application dedicated to modeling, evaluating and visualizing explainable CNN-based models on EEG data for brain/neuroscience research. We have implemented the functions including EEG data loading, model training, evaluation and parameter visualization. The application is also built with a model base including representative convolutional neural network architectures for users to implement without any programming. With its easy-to-use graphic user interface (GUI), it is completely available for investigators of different disciplines with limited resource and limited programming skill. Starting with preprocessed EEG data, users can quickly train the desired model, evaluate the performance, and finally visualize features learned by the model with no pain.\",\"PeriodicalId\":106759,\"journal\":{\"name\":\"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMEW56448.2022.9859322\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMEW56448.2022.9859322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accelerating Brain Research using Explainable Artificial Intelligence
In this demo, we present ExBrainable, an open-source application dedicated to modeling, evaluating and visualizing explainable CNN-based models on EEG data for brain/neuroscience research. We have implemented the functions including EEG data loading, model training, evaluation and parameter visualization. The application is also built with a model base including representative convolutional neural network architectures for users to implement without any programming. With its easy-to-use graphic user interface (GUI), it is completely available for investigators of different disciplines with limited resource and limited programming skill. Starting with preprocessed EEG data, users can quickly train the desired model, evaluate the performance, and finally visualize features learned by the model with no pain.