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

Yudhana, Anton; Muslim, Akbar; Wati, Dewi Eko; Puspitasari, Ika; Azhari, Ahmad; Mardhia, Murein Miksa

doi:10.25046/aj0506131

Cited by 22 publications

(20 citation statements)

References 24 publications

(24 reference statements)

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“… Among classification algorithms the most common choices are: naïve Bayes [158,159,160], Decision Tree [161,162,163], Random Forest [164,165,166], Support Vector Machines [167,168,169], and K Nearest Neighbors [170,171,172].  Among regression algorithms the usual choices are: linear regression [173,174,175], Lasso Regression [176,177], Logistic Regression [178,179,180], Multivariate Regression [181,182], and Multiple Regression Algorithm [183,184].…”

Section: Classificationsmentioning

confidence: 99%

Internet and Biometric Web Based Business Management Decision Support

Kaklauskas¹

2023

Preprint

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Section: Classificationsmentioning

confidence: 99%

Internet and Biometric Web Based Business Management Decision Support

Kaklauskas¹

2023

Preprint

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“…A diverse range of AI algorithms have been applied for AFFECT recognition, for example machine learning, artificial neural networks, search algorithms, expert systems, evolutionary computing, natural language processing, metaheuristics, fuzzy logic, genetic algorithms, and others. Some of the most important supervised (classification, regression), unsupervised (clustering), and reinforcement learning algorithms of machine learning are common as tools in biometrics or neuroscience research to detect emotions and affective attitudes, and are listed below: Among classification algorithms the most common choices are: naïve Bayes [ 158 , 159 , 160 ], Decision Tree [ 161 , 162 , 163 ], Random Forest [ 164 , 165 , 166 ], Support Vector Machines [ 167 , 168 , 169 ], and K Nearest Neighbors [ 170 , 171 , 172 ]. Among regression algorithms the usual choices are: linear regression [ 173 , 174 , 175 ], Lasso Regression [ 176 , 177 ], Logistic Regression [ 178 , 179 , 180 ], Multivariate Regression [ 181 , 182 ], and Multiple Regression Algorithm [ 183 , 184 ].…”

Section: Brain and Biometric Affect Sensorsmentioning

confidence: 99%

“…Among classification algorithms the most common choices are: naïve Bayes [ 158 , 159 , 160 ], Decision Tree [ 161 , 162 , 163 ], Random Forest [ 164 , 165 , 166 ], Support Vector Machines [ 167 , 168 , 169 ], and K Nearest Neighbors [ 170 , 171 , 172 ].…”

Section: Brain and Biometric Affect Sensorsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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“…Further confirmed classifier works are given by the studies [2,21] that show the SVM is capable of reducing the over-fitting risk and produce high efficient measurement. For KNN classifier, it has been experimentally demonstrated in several techniques of data segmentation [37][38][39]. Nevertheless, the KNN parameter used in this study when k is 1 in ratio of 9(training): 1(testing) values in order to evaluate the merged features in physiological measurement and a self-stress assessment.…”

Section: Classifiermentioning

confidence: 99%

The Enhancement on Stress Levels Based on Physiological Signal and Self-Stress Assessment

Zahari¹,

Mustafa²,

Zain³

et al. 2021

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The prolonged stress needs to be determined and controlled before it harms the physical and mental conditions. This research used questionnaire and physiological approaches in determine stress. EEG signal is an electrophysiological signal to analyze the signal features. The standard features used are peak-to-peak values, mean, standard deviation and root means square (RMS). The unique features in this research are Matthew Correlation Coefficient Advanced (MCCA) and multimodal capabilities in the area of frequency and time-frequency analysis are proposed. In the frequency domain, Power Spectral Density (PSD) techniques were applied while Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were utilized to extract seven features based on time-frequency domain. Various methods applied from previous works are still limited by the stress indices. The merged works between quantities score and physiological measurements were enhanced the stress level from three-levels to six stress levels based on music application will be the second contribution. To validate the proposed method and enhance performance between electroencephalogram (EEG) signals and stress score, support vector machine (SVM), random forest (RF), K- nearest neighbor (KNN) classifier is needed. From the finding, RF gained the best performance average accuracy 85% ±10% in Ten-fold and K-fold techniques compared with SVM and KNN.

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

Cited by 22 publications

References 24 publications

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

The Enhancement on Stress Levels Based on Physiological Signal and Self-Stress Assessment

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