Performance of k-NN classifier for emotion detection using EEG signals

Kaundanya, Vaishnavi L.; Patil, Anita; Panat, Ashish

doi:10.1109/iccsp.2015.7322687

Cited by 15 publications

(6 citation statements)

References 3 publications

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“…Feature extraction is then used to determine variables which correlate well with the target emotional states, according to the specific emotional model that is used [19]. Typical feature extraction methods include wavelet transform [44], spectral power features [45], higher order crossings [46], short-time Fourier transform [47], asymmetry index [48] and/or statistical features [49], e.g., mean, standard deviation, variance, quadratic mean, skewness, power or entropy. Finally, a classification method is used to discriminate a particular emotional state from the features.…”

Section: Related Previous Workmentioning

confidence: 99%

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

Sensors

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Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

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Section: Related Previous Workmentioning

confidence: 99%

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

Sensors

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“…Blaiech, Hayfa, et al concluded that a favorable rate of recognition is obtained for joy, anger and disgust emotions and an acceptable rate for four other emotions [5]. Kaundanya, Vaishnavi L. classified emotions using a Knearest neighbor (KNN) classifier and good results are obtained by KNN [6]. Murugappan, Muthusamy considered the classification of discrete emotions rather than dimensional emotions valence/arousal [7].…”

Section: Literature Surveymentioning

confidence: 99%

Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques

Kimmatkar

Babu

2021

Computers

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The aim of this research study is to detect emotional state by processing electroencephalography (EEG) signals and test effect of meditation music therapy to stabilize mental state. This study is useful to identify 12 subtle emotions angry (annoying, angry, nervous), calm (calm, peaceful, relaxed), happy (excited, happy, pleased), sad (sleepy, bored, sad). A total 120 emotion signals were collected by using Emotive 14 channel EEG headset. Emotions are elicited by using three types of stimulus thoughts, audio and video. The system is trained by using captured database of emotion signals which include 30 signals of each emotion class. A total of 24 features were extracted by performing Chirplet transform. Band power is ranked as the prominent feature. The multimodel approach of classifier is used to classify emotions. Classification accuracy is tested for K-nearest neighbor (KNN), convolutional neural network (CNN), recurrent neural network (RNN) and deep neural network (DNN) classifiers. The system is tested to detect emotions of intellectually disable people. Meditation music therapy is used to stable mental state. It is found that it changed emotions of both intellectually disabled and normal participants from the annoying state to the relaxed state. A 75% positive transformation of mental state is obtained in the participants by using music therapy. This research study presents a novel approach for detailed analysis of brain EEG signals for emotion detection and stabilize mental state.

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“…K-nearest neighbors algorithm and its functioning to classify emotions are described by Kaundanya et al (2015). This proposal is a method for EEG signal acquisition tasks, pre-processing, feature extraction, and emotion classification.…”

Section: Related Workmentioning

confidence: 99%

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

López-Hernández

González‐Carrasco

López-Cuadrado

et al. 2021

Front. Neuroinform.

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Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

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Performance of k-NN classifier for emotion detection using EEG signals

Cited by 15 publications

References 3 publications

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Novel Approach for Emotion Detection and Stabilizing Mental State by Using Machine Learning Techniques

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

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