{"title":"Hilbert将基于差分进化的卷积神经网络优化用于脑电图信号的滤波和分类","authors":"Raja Sekhar Banovoth, Kadambari K V","doi":"10.1016/j.compeleceng.2025.110726","DOIUrl":null,"url":null,"abstract":"<div><div>Electroencephalography (EEG) is widely regarded as an effective non-invasive technique for measuring neuronal activity in the human brain, offering high temporal resolution. However, EEG signals are often contaminated by noise and artifacts, which significantly impact their analysis. To tackle this challenge, various advancements in deep learning-based denoising techniques have been developed. Despite these advancements, identifying the optimal network architecture across a broad range of initial parameters remain complex. Manually tuning these parameters to achieve optimal performance is time-intensive and demands substantial expertise. Along the same lines, we introduce a novel residual block-based neural network for automatic artifact removal and a convolution neural network for classification. The architecture unfolds in two distinct stages: Firstly, the noise elimination, where the raw EEG signals undergo the Hilbert transformation, converting them into complex-valued signals. These signals serve as the input for a Residual block-based Convolution Neural Network constructed using the innovative technique of Differential Evolution (HT-DEResNet), which optimizes both the architecture and initial parameters. This enables the effective removal of artifacts without manual intervention. The model is applied across three complex datasets: HaLT, eye artifact, and major depressive disorder. The secondary stage comprises the development of a Multi-Convolution Neural Network (MCNN) for classification. To validate the effectiveness, the BCI competition datasets 2a & 2b are subjected to the HT-DEResNet framework. Subsequently, the signals are classified. The results not only surpass state-of-the-art methods in accuracy but also achieve a 17.73% improvement in performance attributed to noise removal.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110726"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hilbert transformed differential evolution based optimization of convolution neural network for Electroencephalogram signal filtering and classification\",\"authors\":\"Raja Sekhar Banovoth, Kadambari K V\",\"doi\":\"10.1016/j.compeleceng.2025.110726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Electroencephalography (EEG) is widely regarded as an effective non-invasive technique for measuring neuronal activity in the human brain, offering high temporal resolution. However, EEG signals are often contaminated by noise and artifacts, which significantly impact their analysis. To tackle this challenge, various advancements in deep learning-based denoising techniques have been developed. Despite these advancements, identifying the optimal network architecture across a broad range of initial parameters remain complex. Manually tuning these parameters to achieve optimal performance is time-intensive and demands substantial expertise. Along the same lines, we introduce a novel residual block-based neural network for automatic artifact removal and a convolution neural network for classification. The architecture unfolds in two distinct stages: Firstly, the noise elimination, where the raw EEG signals undergo the Hilbert transformation, converting them into complex-valued signals. These signals serve as the input for a Residual block-based Convolution Neural Network constructed using the innovative technique of Differential Evolution (HT-DEResNet), which optimizes both the architecture and initial parameters. This enables the effective removal of artifacts without manual intervention. The model is applied across three complex datasets: HaLT, eye artifact, and major depressive disorder. The secondary stage comprises the development of a Multi-Convolution Neural Network (MCNN) for classification. To validate the effectiveness, the BCI competition datasets 2a & 2b are subjected to the HT-DEResNet framework. Subsequently, the signals are classified. The results not only surpass state-of-the-art methods in accuracy but also achieve a 17.73% improvement in performance attributed to noise removal.</div></div>\",\"PeriodicalId\":50630,\"journal\":{\"name\":\"Computers & Electrical Engineering\",\"volume\":\"128 \",\"pages\":\"Article 110726\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Electrical Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S004579062500669X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S004579062500669X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Hilbert transformed differential evolution based optimization of convolution neural network for Electroencephalogram signal filtering and classification
Electroencephalography (EEG) is widely regarded as an effective non-invasive technique for measuring neuronal activity in the human brain, offering high temporal resolution. However, EEG signals are often contaminated by noise and artifacts, which significantly impact their analysis. To tackle this challenge, various advancements in deep learning-based denoising techniques have been developed. Despite these advancements, identifying the optimal network architecture across a broad range of initial parameters remain complex. Manually tuning these parameters to achieve optimal performance is time-intensive and demands substantial expertise. Along the same lines, we introduce a novel residual block-based neural network for automatic artifact removal and a convolution neural network for classification. The architecture unfolds in two distinct stages: Firstly, the noise elimination, where the raw EEG signals undergo the Hilbert transformation, converting them into complex-valued signals. These signals serve as the input for a Residual block-based Convolution Neural Network constructed using the innovative technique of Differential Evolution (HT-DEResNet), which optimizes both the architecture and initial parameters. This enables the effective removal of artifacts without manual intervention. The model is applied across three complex datasets: HaLT, eye artifact, and major depressive disorder. The secondary stage comprises the development of a Multi-Convolution Neural Network (MCNN) for classification. To validate the effectiveness, the BCI competition datasets 2a & 2b are subjected to the HT-DEResNet framework. Subsequently, the signals are classified. The results not only surpass state-of-the-art methods in accuracy but also achieve a 17.73% improvement in performance attributed to noise removal.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.