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- W2801066935 abstract "Emotions play an important role in our daily life. AntonioDamasiofamously stated: “We are notthinking machines that feel; rather we are feeling machines that think”. Human emotions can berecognized through facial expression, speech and gesture. The use of electroencephalograms (EEGs)to understand and recognize human emotion has been widely studied, where those recognitiontechniques greatly benefit in human-computer interaction (HCI). In this investigation, we study theuse of EEGs to recognize emotions. Pleasant and unpleasant images are used as stimuli to elicithuman pleasant and unpleasant emotions.The brainwaves are recorded using a 9-electrode medicalgrade wireless EEG headset from Advance Brain Monitoring (ABM), the B-alert X10. The featurescomprising alpha, beta, gamma, theta, and delta bands are then extracted from the recordedbrainwaves using time-frequency analysis. Different channels and rhythms are used in support vectormachine (SVM) and K-nearest neighbors (KNN) classifiers to train and classify pleasant vs.unpleasant mind states. The best accuracy obtained was 70.43% using SVM with alpha and betarhythmsfromchannel F3 and Fz, and also with gamma rhythms from channel POz." @default.
- W2801066935 created "2018-05-17" @default.
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- W2801066935 date "2015-01-01" @default.
- W2801066935 modified "2023-09-26" @default.
- W2801066935 title "EEG-based recognition of positive and negative emotions using for pleasant vs. unpleasant images" @default.
- W2801066935 hasPublicationYear "2015" @default.
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