Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387092404> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W4387092404 endingPage "9" @default.
- W4387092404 startingPage "1" @default.
- W4387092404 abstract "The classification problem for short time-window steady-state visual evoked potentials (SSVEPs) is important in practical applications because shorter time-window often means faster response speed. By combining the advantages of the local feature learning ability of convolutional neural network (CNN) and the feature importance distinguishing ability of attention mechanism, a novel network called AttentCNN is proposed to further improve the classification performance for short time-window SSVEP. Considering the frequency-domain features extracted from short time-window signals are not obvious, this network starts with the time-domain feature extraction module based on the filter bank (FB). The FB consists of four sixth-order Butterworth filters with different bandpass ranges. Then extracted multimodal features are aggregated together. The second major module is a set of residual squeeze and excitation blocks (RSEs) that has the ability to improve the quality of extracted features by learning the interdependence between features. The final major module is time-domain CNN (tCNN) that consists of four CNNs for further feature extraction and followed by a fully connected (FC) layer for output. Our designed networks are validated over two large public datasets, and necessary comparisons are given to verify the effectiveness and superiority of the proposed network. In the end, in order to demonstrate the application potential of the proposed strategy in the medical rehabilitation field, we design a novel five-finger bionic hand and connect it to our trained network to achieve the control of bionic hand by human brain signals directly. Our source codes are available on Github: https://github.com/JiannanChen/AggtCNN.git." @default.
- W4387092404 created "2023-09-28" @default.
- W4387092404 creator A5026904801 @default.
- W4387092404 creator A5046826631 @default.
- W4387092404 creator A5051363890 @default.
- W4387092404 creator A5055546056 @default.
- W4387092404 creator A5064692183 @default.
- W4387092404 creator A5081117163 @default.
- W4387092404 date "2023-01-01" @default.
- W4387092404 modified "2023-09-29" @default.
- W4387092404 title "Attention-Based Multimodal tCNN for Classification of Steady-State Visual Evoked Potentials and Its Application to Gripper Control" @default.
- W4387092404 doi "https://doi.org/10.1109/tnnls.2023.3313691" @default.
- W4387092404 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37756172" @default.
- W4387092404 hasPublicationYear "2023" @default.
- W4387092404 type Work @default.
- W4387092404 citedByCount "0" @default.
- W4387092404 crossrefType "journal-article" @default.
- W4387092404 hasAuthorship W4387092404A5026904801 @default.
- W4387092404 hasAuthorship W4387092404A5046826631 @default.
- W4387092404 hasAuthorship W4387092404A5051363890 @default.
- W4387092404 hasAuthorship W4387092404A5055546056 @default.
- W4387092404 hasAuthorship W4387092404A5064692183 @default.
- W4387092404 hasAuthorship W4387092404A5081117163 @default.
- W4387092404 hasConcept C103824480 @default.
- W4387092404 hasConcept C106131492 @default.
- W4387092404 hasConcept C138885662 @default.
- W4387092404 hasConcept C153180895 @default.
- W4387092404 hasConcept C154945302 @default.
- W4387092404 hasConcept C2776401178 @default.
- W4387092404 hasConcept C28490314 @default.
- W4387092404 hasConcept C31972630 @default.
- W4387092404 hasConcept C41008148 @default.
- W4387092404 hasConcept C41895202 @default.
- W4387092404 hasConcept C52622490 @default.
- W4387092404 hasConcept C81363708 @default.
- W4387092404 hasConceptScore W4387092404C103824480 @default.
- W4387092404 hasConceptScore W4387092404C106131492 @default.
- W4387092404 hasConceptScore W4387092404C138885662 @default.
- W4387092404 hasConceptScore W4387092404C153180895 @default.
- W4387092404 hasConceptScore W4387092404C154945302 @default.
- W4387092404 hasConceptScore W4387092404C2776401178 @default.
- W4387092404 hasConceptScore W4387092404C28490314 @default.
- W4387092404 hasConceptScore W4387092404C31972630 @default.
- W4387092404 hasConceptScore W4387092404C41008148 @default.
- W4387092404 hasConceptScore W4387092404C41895202 @default.
- W4387092404 hasConceptScore W4387092404C52622490 @default.
- W4387092404 hasConceptScore W4387092404C81363708 @default.
- W4387092404 hasFunder F4320321001 @default.
- W4387092404 hasLocation W43870924041 @default.
- W4387092404 hasLocation W43870924042 @default.
- W4387092404 hasOpenAccess W4387092404 @default.
- W4387092404 hasPrimaryLocation W43870924041 @default.
- W4387092404 hasRelatedWork W1964120219 @default.
- W4387092404 hasRelatedWork W2144059113 @default.
- W4387092404 hasRelatedWork W2146076056 @default.
- W4387092404 hasRelatedWork W2406522397 @default.
- W4387092404 hasRelatedWork W2546942002 @default.
- W4387092404 hasRelatedWork W2767651786 @default.
- W4387092404 hasRelatedWork W2811390910 @default.
- W4387092404 hasRelatedWork W2913302899 @default.
- W4387092404 hasRelatedWork W3003836766 @default.
- W4387092404 hasRelatedWork W4312376745 @default.
- W4387092404 isParatext "false" @default.
- W4387092404 isRetracted "false" @default.
- W4387092404 workType "article" @default.