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

Kimmatkar, Nisha Vishnupant; Babu, B. Vijaya

doi:10.3390/computers10030037

Cited by 27 publications

(17 citation statements)

References 17 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research with neurophysiological sensors [ 12 , 13 ] has begun to aid in monitoring schizophrenia [ 14 , 15 ], Parkinson’s’ disease [ 16 ], traumatic brain injury [ 17 ], physiological signals [ 18 ], cognitive function [ 19 ], epileptic seizures [ 20 ], alcoholism [ 21 ], brain tumors [ 22 ], brain cancer [ 23 ], mental stability [ 24 ], personality [ 25 ], eye tracking [ 26 ], and many other phenomena. Moreover, studies conducted on EEG data processing have identified human emotions with exceptional accuracy [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Literature Reviewmentioning

confidence: 99%

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Akter

Prodhan

Pias

et al. 2022

Sensors

View full text Add to dashboard Cite

Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Akter

Prodhan

Pias

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…However, such models are still unable to achieve a respectable level of accuracy due to the small and noisy datasets. Despite the dataset being very dimensional, low SVM performance is caused by overlap in target classes and boundaries [2].…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis

Kimmatkar

Babu

2022

IJRITCC

View full text Add to dashboard Cite

With electroencephalography (EEG) brain waves alone, it is full-scale phenomena in the field of computer-brain interface DNN, CNN, and SVM have improved detection and prediction accuracy in a number of researches during the last several years. But when it comes to recognizing global reliance, both deep learning and SVM have obvious limits. Pre-processing, extraction capabilities, and network design are the most common techniques used in deep learning models today, yet they are still unable to produce reliable results in noisy and sparse datasets. Any dataset, no matter how little or large, may suffer from poor SVM performance due to overlapping target instructions and boundaries. There are many different sorts of emotions that may be classified using the particular approach employed in this research. In order to get a whole picture of a person's mental state, it is best to use a "Min of mean” proposed technique. After comparison to the referential mean, a feeling is divided into one of four emotional quadrants. The MIN Max range is used to further split the emotion into 12 subcategories based on the amount of arousal. The proposed set of rules performed better than existing methods. Research on multi-class emotion reputation has shown that, compared to more recent studies, the proposed technique may be rather strong. It is possible to analyze a person's mental health by using the emotional spectrum, which has an accuracy rate of above 90%.

show abstract

“…In this regard, the use of a multiparametric model is introduced in this paper in order to differentiate whether there are differences between the performance of AB in the attention context using different machine learning techniques over EEG signals. Such algorithms have shown to be effective in emotionally oriented tasks [15][16][17][18], cognitive stress detection [19,20] and others by using EEG signals alone [21,22] or in conjunction with other physiological signals such as electrodermal activity (EDA) [23,24], blood volume pressure (BVP) [25], electrocardiogram (ECG) and electromyography (EMG), among others [26][27][28]. Therefore, the aim of this pilot study has been twofold.…”

Section: Introductionmentioning

confidence: 99%

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

et al. 2022

View full text Add to dashboard Cite

Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.

show abstract

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

Cited by 27 publications

References 17 publications

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

Contact Info

Product

Resources

About