FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384201179> ?p ?o ?g. }

• W4384201179 abstract "Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance." @default.
• W4384201179 created "2023-07-14" @default.
• W4384201179 creator A5026707386 @default.
• W4384201179 creator A5027894205 @default.
• W4384201179 date "2023-07-13" @default.
• W4384201179 modified "2023-10-16" @default.
• W4384201179 title "Survey on the research direction of EEG-based signal processing" @default.
• W4384201179 cites W1963671300 @default.
• W4384201179 cites W1969140390 @default.
• W4384201179 cites W1978597259 @default.
• W4384201179 cites W1986516294 @default.
• W4384201179 cites W2025758384 @default.
• W4384201179 cites W2034853265 @default.
• W4384201179 cites W2055603681 @default.
• W4384201179 cites W2112563323 @default.
• W4384201179 cites W2126686521 @default.
• W4384201179 cites W2128495200 @default.
• W4384201179 cites W2145302786 @default.
• W4384201179 cites W2145947508 @default.
• W4384201179 cites W2151554678 @default.
• W4384201179 cites W2203224402 @default.
• W4384201179 cites W2247648522 @default.
• W4384201179 cites W2346353886 @default.
• W4384201179 cites W2725546079 @default.
• W4384201179 cites W2759030247 @default.
• W4384201179 cites W2791830995 @default.
• W4384201179 cites W2795923674 @default.
• W4384201179 cites W2891503716 @default.
• W4384201179 cites W2894424125 @default.
• W4384201179 cites W2896120927 @default.
• W4384201179 cites W2989145158 @default.
• W4384201179 cites W3004859010 @default.
• W4384201179 cites W3009082884 @default.
• W4384201179 cites W3012521722 @default.
• W4384201179 cites W3046474724 @default.
• W4384201179 cites W3082414409 @default.
• W4384201179 cites W3083218890 @default.
• W4384201179 cites W3088698541 @default.
• W4384201179 cites W3095244751 @default.
• W4384201179 cites W3102455230 @default.
• W4384201179 cites W3102856398 @default.
• W4384201179 cites W3118932394 @default.
• W4384201179 cites W3127935830 @default.
• W4384201179 cites W3129685057 @default.
• W4384201179 cites W3133598629 @default.
• W4384201179 cites W3135670473 @default.
• W4384201179 cites W3139270893 @default.
• W4384201179 cites W3154555745 @default.
• W4384201179 cites W3159746424 @default.
• W4384201179 cites W3164194633 @default.
• W4384201179 cites W3164602583 @default.
• W4384201179 cites W3166991312 @default.
• W4384201179 cites W3168785516 @default.
• W4384201179 cites W3174766882 @default.
• W4384201179 cites W3177214986 @default.
• W4384201179 cites W3190460267 @default.
• W4384201179 cites W3193603015 @default.
• W4384201179 cites W3194868538 @default.
• W4384201179 cites W3202099927 @default.
• W4384201179 cites W3204693088 @default.
• W4384201179 cites W3205392914 @default.
• W4384201179 cites W3206297467 @default.
• W4384201179 cites W3206730562 @default.
• W4384201179 cites W3208303146 @default.
• W4384201179 cites W3209093608 @default.
• W4384201179 cites W3211972444 @default.
• W4384201179 cites W3212834234 @default.
• W4384201179 cites W4200389064 @default.
• W4384201179 cites W4200409924 @default.
• W4384201179 cites W4200524479 @default.
• W4384201179 cites W4200532932 @default.
• W4384201179 cites W4205521451 @default.
• W4384201179 cites W4205568306 @default.
• W4384201179 cites W4205747909 @default.
• W4384201179 cites W4205976935 @default.
• W4384201179 cites W4206759433 @default.
• W4384201179 cites W4210266523 @default.
• W4384201179 cites W4211043739 @default.
• W4384201179 cites W4214812564 @default.
• W4384201179 cites W4220730662 @default.
• W4384201179 cites W4221074078 @default.
• W4384201179 cites W4223988676 @default.
• W4384201179 cites W4224660121 @default.
• W4384201179 cites W4226206056 @default.
• W4384201179 cites W4229049930 @default.
• W4384201179 cites W4229068725 @default.
• W4384201179 cites W4281656768 @default.
• W4384201179 cites W4281934495 @default.
• W4384201179 cites W4282547510 @default.
• W4384201179 cites W4282827363 @default.
• W4384201179 cites W4283010101 @default.
• W4384201179 cites W4283069747 @default.
• W4384201179 cites W4283386431 @default.
• W4384201179 cites W4294975762 @default.
• W4384201179 doi "https://doi.org/10.3389/fnins.2023.1203059" @default.
• W4384201179 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37521708" @default.
• W4384201179 hasPublicationYear "2023" @default.
• W4384201179 type Work @default.
• W4384201179 citedByCount "1" @default.
• W4384201179 countsByYear W43842011792023 @default.

• next