Open AccessImproving Accuracy of Emotion Detection using Brain Waves and Adaptive Swarm Intelligence
Author(s) -
Ruchita Timande*,
Prof. Payal Ghutke
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4163.049620
Subject(s) - computer science , electroencephalography , redundancy (engineering) , artificial intelligence , feature (linguistics) , pattern recognition (psychology) , principal component analysis , relevance (law) , speech recognition , machine learning , psychology , linguistics , philosophy , psychiatry , political science , law , operating system
In recent year, Authors had been attempting to find or detect the feeling of human by recorded brain signal for example, EEG (electroencephalogram) alerts. Because of the unnecessary degrees of unwanted signal from EEG recording, a solitary feature alone can't accomplish great execution. Distinct feature is key for automatic feeling identification. Right now, we present an AI based scheme utilizing various features extricated from EEG recordings. The plan joins these particular highlights in feature space utilizing both managed and unaided component choice procedures. To re-request the joined highlights to max-importance with the names and min-repetition of each feature by applying Maximum Relevance Minimum Redundancy (MRMR). The produced highlights are additionally diminished with principal component analysis(PCA) for removing essential segments. Test report will be generated to show that the proposed work should outperform the condition of-workmanship techniques utilizing similar settings in real time dataset.