Initial analysis of brain EEG signal for mental state detection of human being

Kimmatkar, Nisha Vishnupant; Babu, B. Vijaya

doi:10.1109/icoei.2017.8300934

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…The files were edited and only the 14 electrode data were saved in .csv format. The dimension of the database is 120*[128*60]* [14]. The dataset comprised of 120 files and 30 emotion signals of each emotion type (Angry, Calm, Happy, and Sad).…”

Section: A Database Creationmentioning

confidence: 99%

“…The dataset comprised of 120 files and 30 emotion signals of each emotion type (Angry, Calm, Happy, and Sad). [128*60] rows signals were recorded for 1min at 128 fs, [14] col represents the e1.lectrodes. The signals were preprocessed using filtering.…”

Section: A Database Creationmentioning

confidence: 99%

“…ICA is a technique for separating data artifacts (since they are usually independent of each other) [3]. Statistical modeling [5] [14] characteristics were considered. Figure 1 depicts the followed multimodal method.…”

Section: A Database Creationmentioning

confidence: 99%

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Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Kimmatkar

Babu²

2022

Eng. Technol. Appl. Sci. Res.

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Biological brain signals may be used to identify emotions in a variety of ways, with accuracy depended on the methods used for signal processing, feature extraction, feature selection, and classification. The major goal of the current work was to use an adaptive channel selection and classification strategy to improve the effectiveness of emotion detection utilizing brain signals. Using different features picked by feature fusion approaches, the accuracy of existing classification models' emotion detection is assessed. Statistical modeling is used to determine time-domain and frequency-domain properties. Multiclass classification accuracy is examined using Neural Networks (NNs), Lasso regression, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). After performing hyperparameter tuning, a remarkable increase in accuracy is achieved using Lasso regression, while RF performed well for all the feature sets. 78.02% and 76.77% accuracy were achieved for a small and noisy 24 feature dataset by Lasso regression and RF respectively whereas 76.54% accuracy is achieved by Lasso regression with the backward elimination wrapper method.

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Section: A Database Creationmentioning

confidence: 99%

Section: A Database Creationmentioning

confidence: 99%

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Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Kimmatkar

Babu²

2022

Eng. Technol. Appl. Sci. Res.

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“…[2] In response to drowsiness or alertness, the temperature of a miniature room's air conditioning and the light intensity would automatically adjust. A further application that was explored in 2016 was in the use of an EEG to assess stress levels in an individual, funded by the Ministry of Education in Malaysia [3]. By synthesizing the readout information from EEG and functional near-infrared spectroscopy (fNIRS), the researchers classified brain activities with a support vector machine (SVM) in order to attempt to improve upon previous methods of stress detection through non-invasive brain machine interfaces.…”

Section: Introductionmentioning

confidence: 99%

Mental State Detection in Classroom Based on EEG Brain Signals

Gong¹,

Xu²

2019

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The goal of this work is to identify human brain waves in different states non-invasively, and to distinguish them into different levels of mental states in order to provide immediate mental state feedback to a classroom instructor and maximize learning outcomes. In order to apply such knowledge, this project utilizes a commercial NeuroSky Mindwave Mobile EEG to collect brain signals, MATLAB to filter data, voltage thresholds to detect blinks, which are used in tandem with power spectral density (PSD) analysis in order to classify mental states. This knowledge can then be provided to a class instructor who can use it to maximize the learning experience for students.

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“…Hjorth activity, HM-Hjorth mobility, HC-16. Hjorth complexity, THD-harmonic Distortion, PSD-power spectral density, HP-parameter, VEEG-variance of EEG, DASTD-difference absolute standard deviation value mean 2, PXX-autoregressive[22].…”

mentioning

confidence: 99%

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

Kimmatkar

Babu

2021

Computers

Self Cite

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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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Initial analysis of brain EEG signal for mental state detection of human being

Cited by 6 publications

References 17 publications

Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Human Emotion Detection with Electroencephalography Signals and Accuracy Analysis Using Feature Fusion Techniques and a Multimodal Approach for Multiclass Classification

Mental State Detection in Classroom Based on EEG Brain Signals

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

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