Matches in SemOpenAlex for { <https://semopenalex.org/work/W4294250373> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W4294250373 endingPage "603" @default.
- W4294250373 startingPage "591" @default.
- W4294250373 abstract "In recent years, emotion recognition based on electroencephalogram (EEG) has gained prominence due to its wide applications in the area of healthcare, affective computing, brain-computer interface, etc. Capturing the emotion quotient effectively and thus improving the recognition performance has been a major challenge in the conventional emotion recognition problem based on EEG. This work presents a new automatic emotion recognition algorithm using hybrid multi-channel EEG features and a Grid Search Random Forest (GSRF). The proposed algorithm extracts multi-domain features from different channels of the EEG signal and fused them into a hybrid feature matrix. A GSRF has been fed with a labeled feature matrix to classify the emotions into different classes. The algorithm has been validated on two widely used open-source databases DEAP and SEED. The proposed algorithm obtained an average classification accuracy of 86.3% and 97.9% using DEAP and SEED, respectively, with tenfold cross-validation. As compared to the Random Forest classifier, the proposed approach exhibited superior emotion recognition performance." @default.
- W4294250373 created "2022-09-02" @default.
- W4294250373 creator A5004894915 @default.
- W4294250373 creator A5043924482 @default.
- W4294250373 creator A5082692383 @default.
- W4294250373 date "2022-01-01" @default.
- W4294250373 modified "2023-09-26" @default.
- W4294250373 title "Electroencephalogram-Based Emotion Recognition Using Random Forest" @default.
- W4294250373 cites W1947251450 @default.
- W4294250373 cites W2002055708 @default.
- W4294250373 cites W2078332635 @default.
- W4294250373 cites W2091349671 @default.
- W4294250373 cites W2146010402 @default.
- W4294250373 cites W2149628368 @default.
- W4294250373 cites W2150692529 @default.
- W4294250373 cites W2167716931 @default.
- W4294250373 cites W2258245936 @default.
- W4294250373 cites W2593999683 @default.
- W4294250373 cites W2599308356 @default.
- W4294250373 cites W2749183303 @default.
- W4294250373 cites W2790814155 @default.
- W4294250373 cites W2902877680 @default.
- W4294250373 cites W2903462437 @default.
- W4294250373 cites W2922188941 @default.
- W4294250373 cites W2952286992 @default.
- W4294250373 cites W2960283727 @default.
- W4294250373 cites W2963009172 @default.
- W4294250373 cites W2964346239 @default.
- W4294250373 cites W2983840038 @default.
- W4294250373 cites W2989989812 @default.
- W4294250373 cites W3014215018 @default.
- W4294250373 cites W4248024311 @default.
- W4294250373 doi "https://doi.org/10.1007/978-981-19-1520-8_48" @default.
- W4294250373 hasPublicationYear "2022" @default.
- W4294250373 type Work @default.
- W4294250373 citedByCount "0" @default.
- W4294250373 crossrefType "book-chapter" @default.
- W4294250373 hasAuthorship W4294250373A5004894915 @default.
- W4294250373 hasAuthorship W4294250373A5043924482 @default.
- W4294250373 hasAuthorship W4294250373A5082692383 @default.
- W4294250373 hasConcept C118552586 @default.
- W4294250373 hasConcept C138885662 @default.
- W4294250373 hasConcept C153180895 @default.
- W4294250373 hasConcept C154945302 @default.
- W4294250373 hasConcept C15744967 @default.
- W4294250373 hasConcept C169258074 @default.
- W4294250373 hasConcept C173201364 @default.
- W4294250373 hasConcept C206310091 @default.
- W4294250373 hasConcept C2776401178 @default.
- W4294250373 hasConcept C2777438025 @default.
- W4294250373 hasConcept C28490314 @default.
- W4294250373 hasConcept C41008148 @default.
- W4294250373 hasConcept C41895202 @default.
- W4294250373 hasConcept C522805319 @default.
- W4294250373 hasConcept C52622490 @default.
- W4294250373 hasConcept C6438553 @default.
- W4294250373 hasConcept C95623464 @default.
- W4294250373 hasConceptScore W4294250373C118552586 @default.
- W4294250373 hasConceptScore W4294250373C138885662 @default.
- W4294250373 hasConceptScore W4294250373C153180895 @default.
- W4294250373 hasConceptScore W4294250373C154945302 @default.
- W4294250373 hasConceptScore W4294250373C15744967 @default.
- W4294250373 hasConceptScore W4294250373C169258074 @default.
- W4294250373 hasConceptScore W4294250373C173201364 @default.
- W4294250373 hasConceptScore W4294250373C206310091 @default.
- W4294250373 hasConceptScore W4294250373C2776401178 @default.
- W4294250373 hasConceptScore W4294250373C2777438025 @default.
- W4294250373 hasConceptScore W4294250373C28490314 @default.
- W4294250373 hasConceptScore W4294250373C41008148 @default.
- W4294250373 hasConceptScore W4294250373C41895202 @default.
- W4294250373 hasConceptScore W4294250373C522805319 @default.
- W4294250373 hasConceptScore W4294250373C52622490 @default.
- W4294250373 hasConceptScore W4294250373C6438553 @default.
- W4294250373 hasConceptScore W4294250373C95623464 @default.
- W4294250373 hasLocation W42942503731 @default.
- W4294250373 hasOpenAccess W4294250373 @default.
- W4294250373 hasPrimaryLocation W42942503731 @default.
- W4294250373 hasRelatedWork W2025917256 @default.
- W4294250373 hasRelatedWork W2788007871 @default.
- W4294250373 hasRelatedWork W2902877680 @default.
- W4294250373 hasRelatedWork W2980067186 @default.
- W4294250373 hasRelatedWork W3033658423 @default.
- W4294250373 hasRelatedWork W3043942984 @default.
- W4294250373 hasRelatedWork W3046065781 @default.
- W4294250373 hasRelatedWork W4255476263 @default.
- W4294250373 hasRelatedWork W4318831197 @default.
- W4294250373 hasRelatedWork W4366374509 @default.
- W4294250373 isParatext "false" @default.
- W4294250373 isRetracted "false" @default.
- W4294250373 workType "book-chapter" @default.