Peer Review_Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

Yudhana, Anton (2020) Peer Review_Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor. ASTES.

Text 13_Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor.pdf Download (1MB)

Abstract

Human emotional states can transform naturally and are recognizable through facial
expressions, voices, or body movements, influenced by received stimuli. However, the
articulation of emotions is not practicable by every individual, even when feelings of joy,
sadness, or otherwise are experienced. Biomedically, emotions affect brain wave activities,
as the continuously functioning brain cells communicate through electrical pulsations.
Therefore, an electroencephalogram (EEG) is used to capture input from brain signals, study
impulses, and determine the human mood. The examination generally included observing a
person's frame of mind in response to a given stimulus where the immediate results were
inconclusive. In this study, the associated classifications were normal, focused, sad, and
shocked. The raw brainwave data from 50 subjects were recorded by employing a singlechannel EEG called the Neurosky Mindwave. Meanwhile, the assessments were performed
while the candidates’ minds were stimulated by listening to music, watching videos, or
reading books. The Fast Fourier Transform (FFT) method was utilized for feature extractions,
along with the K-nearest neighbours (K-NN) for classifying brain impulses. The parameter k
had a value of 15, and the average classification accuracy was 83.33%, while the highest
accuracy for the focused emotional state was 93.33%. The Neurosky Mindwave in conjunction
with the FFT and KNN techniques is potential analytical solutions to facilitate the enhanced
identification of human emotional conditions

Item Type:	Other
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	Anton Yudhana,
Date Deposited:	15 Jul 2022 02:14
Last Modified:	18 Jul 2022 04:54
URI:	http://eprints.uad.ac.id/id/eprint/35861

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