{"title":"长短时记忆人工神经网络、合成数据和微调如何改进原始脑电图数据的分类","authors":"A. Nasybullin, V. Maksimenko, S. Kurkin","doi":"10.1109/DCNA56428.2022.9923206","DOIUrl":null,"url":null,"abstract":"In this paper, we discuss a Machine Learning pipeline for the classification of EEG data. We propose a combination of synthetic data generation, long short-term memory artificial neural network (LSTM), and fine-tuning to solve classification problems for experiments with implicit visual stimuli, such as the Necker cube with different levels of ambiguity. The developed approach increased the quality of the classification model of raw EEG data.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data\",\"authors\":\"A. Nasybullin, V. Maksimenko, S. Kurkin\",\"doi\":\"10.1109/DCNA56428.2022.9923206\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we discuss a Machine Learning pipeline for the classification of EEG data. We propose a combination of synthetic data generation, long short-term memory artificial neural network (LSTM), and fine-tuning to solve classification problems for experiments with implicit visual stimuli, such as the Necker cube with different levels of ambiguity. The developed approach increased the quality of the classification model of raw EEG data.\",\"PeriodicalId\":110836,\"journal\":{\"name\":\"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCNA56428.2022.9923206\",\"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 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCNA56428.2022.9923206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data
In this paper, we discuss a Machine Learning pipeline for the classification of EEG data. We propose a combination of synthetic data generation, long short-term memory artificial neural network (LSTM), and fine-tuning to solve classification problems for experiments with implicit visual stimuli, such as the Necker cube with different levels of ambiguity. The developed approach increased the quality of the classification model of raw EEG data.