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

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.

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