Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200455726> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4200455726 abstract "Emotion awareness is one of the most important subjects in the field of affective computing. Using nonverbal behavioral methods such as recognition of facial expression, verbal behavioral method, recognition of speech emotion, or physiological signals-based methods such as recognition of emotions based on electroencephalogram (EEG) can predict human emotion. However, it is notable that data obtained from either nonverbal or verbal behaviors are indirect emotional signals suggesting brain activity. Unlike the nonverbal or verbal actions, EEG signals are reported directly from the human brain cortex and thus may be more effective in representing the inner emotional states of the brain. Consequently, when used to measure human emotion, the use of EEG data can be more accurate than data on behavior. For this reason, the identification of human emotion from EEG signals has become a very important research subject in current emotional brain-computer interfaces (BCIs) aimed at inferring human emotional states based on the EEG signals recorded. In this paper, a hybrid deep learning approach has proposed using CNN and a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic classification of epileptic disease from EEG signals. The signals have been processed by CNN for feature extraction from runtime environment while LSTM has used for classification of entire data. Finally, system demonstrates each EEG data file as normal or epileptic disease. In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms. This research demonstrates a collaboration of CNN and LSTM for entire classification of EEG signals in numerous existing systems." @default.
- W4200455726 created "2021-12-31" @default.
- W4200455726 creator A5051224280 @default.
- W4200455726 creator A5067851398 @default.
- W4200455726 date "2021-12-01" @default.
- W4200455726 modified "2023-09-30" @default.
- W4200455726 title "A Review on BCI Emotions Classification for EEG Signals Using Deep Learning" @default.
- W4200455726 doi "https://doi.org/10.3233/apc210241" @default.
- W4200455726 hasPublicationYear "2021" @default.
- W4200455726 type Work @default.
- W4200455726 citedByCount "1" @default.
- W4200455726 countsByYear W42004557262023 @default.
- W4200455726 crossrefType "book-chapter" @default.
- W4200455726 hasAuthorship W4200455726A5051224280 @default.
- W4200455726 hasAuthorship W4200455726A5067851398 @default.
- W4200455726 hasBestOaLocation W42004557261 @default.
- W4200455726 hasConcept C108583219 @default.
- W4200455726 hasConcept C138885662 @default.
- W4200455726 hasConcept C145633318 @default.
- W4200455726 hasConcept C153180895 @default.
- W4200455726 hasConcept C154945302 @default.
- W4200455726 hasConcept C15744967 @default.
- W4200455726 hasConcept C169760540 @default.
- W4200455726 hasConcept C173201364 @default.
- W4200455726 hasConcept C195704467 @default.
- W4200455726 hasConcept C206310091 @default.
- W4200455726 hasConcept C2776401178 @default.
- W4200455726 hasConcept C2777670902 @default.
- W4200455726 hasConcept C28490314 @default.
- W4200455726 hasConcept C41008148 @default.
- W4200455726 hasConcept C41895202 @default.
- W4200455726 hasConcept C46312422 @default.
- W4200455726 hasConcept C522805319 @default.
- W4200455726 hasConcept C52622490 @default.
- W4200455726 hasConcept C6438553 @default.
- W4200455726 hasConceptScore W4200455726C108583219 @default.
- W4200455726 hasConceptScore W4200455726C138885662 @default.
- W4200455726 hasConceptScore W4200455726C145633318 @default.
- W4200455726 hasConceptScore W4200455726C153180895 @default.
- W4200455726 hasConceptScore W4200455726C154945302 @default.
- W4200455726 hasConceptScore W4200455726C15744967 @default.
- W4200455726 hasConceptScore W4200455726C169760540 @default.
- W4200455726 hasConceptScore W4200455726C173201364 @default.
- W4200455726 hasConceptScore W4200455726C195704467 @default.
- W4200455726 hasConceptScore W4200455726C206310091 @default.
- W4200455726 hasConceptScore W4200455726C2776401178 @default.
- W4200455726 hasConceptScore W4200455726C2777670902 @default.
- W4200455726 hasConceptScore W4200455726C28490314 @default.
- W4200455726 hasConceptScore W4200455726C41008148 @default.
- W4200455726 hasConceptScore W4200455726C41895202 @default.
- W4200455726 hasConceptScore W4200455726C46312422 @default.
- W4200455726 hasConceptScore W4200455726C522805319 @default.
- W4200455726 hasConceptScore W4200455726C52622490 @default.
- W4200455726 hasConceptScore W4200455726C6438553 @default.
- W4200455726 hasLocation W42004557261 @default.
- W4200455726 hasOpenAccess W4200455726 @default.
- W4200455726 hasPrimaryLocation W42004557261 @default.
- W4200455726 hasRelatedWork W2016461833 @default.
- W4200455726 hasRelatedWork W2115206060 @default.
- W4200455726 hasRelatedWork W2128739463 @default.
- W4200455726 hasRelatedWork W2384010565 @default.
- W4200455726 hasRelatedWork W2467810721 @default.
- W4200455726 hasRelatedWork W2733060750 @default.
- W4200455726 hasRelatedWork W2773120646 @default.
- W4200455726 hasRelatedWork W2945144746 @default.
- W4200455726 hasRelatedWork W3033658423 @default.
- W4200455726 hasRelatedWork W4317987726 @default.
- W4200455726 isParatext "false" @default.
- W4200455726 isRetracted "false" @default.
- W4200455726 workType "book-chapter" @default.