Evolutionary inspired approach for mental stress detection using EEG signal

Sharma, Lakhan Dev; Bohat, Vijay Kumar; Habib, Maria; Al-Zoubi, Ala’ M.; Faris, Hossam; Aljarah, Ibrahim

doi:10.1016/j.eswa.2022.116634

Cited by 53 publications

(7 citation statements)

References 46 publications

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“…With the rapid development of EEG signal acquisition technology, brain-computer interface technology, and artificial intelligence technology, the study of EEG signals has gotten great attention in many countries. [22][23][24][25][26] e US government launched the Human Brain Initiative in 2013 to explore brain mechanisms, advance neuroscience research and develop new treatments for brain diseases that currently have no cure. In the same year, the European Union and Japan also announced their respective "Brain Project".…”

Section: Introductionmentioning

confidence: 99%

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

Liang¹

2022

Scientific Programming

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With the development of information technology, music education in universities is also changing. Traditional music education can not effectively explore the feature of students, resulting in the quality of music education being restricted. The rapid development of Electroencephalogram (EEG) signals has brought a new educational model to music education. Through the extraction of students’ psychological features of music by EEG, psychological features can be identified and different educational programs can be formulated according to the results. Multifeature extraction and combination method can improve the accuracy of EEG feature extraction. Using empirical mode decomposition and wavelet packet decomposition of the two kinds of methods to analyze EEG data, respectively, then the average energy, volatility index, sample entropy, and approximate entropy and multiscale features such as permutation entropy and Hurst index, select features in combination, to classify the feature set after the combination, so as to find out the feature of the performance of the optimal combination. The experimental results show that the feature combination of sample entropy and approximate entropy can better represent the main features of EEG psychological characteristic signals after wavelet packet decomposition, and the recognition accuracy is more than 90%.

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Section: Introductionmentioning

confidence: 99%

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

Liang¹

2022

Scientific Programming

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“…The Hilbert transform, often known as the HT, is an essential part of the HVD approach, as the name of this technique suggests. For the purpose of signal decomposition of EEG data, Sharma et al [ 62 , 63 ] employed the sliding window transform (SWT) and sliding mode-SSA (SM-SSA) [ 64 ]. In [ 65 ], Multivariant variational mode decomposition (MVMD) and PCA were used for EOG artifact elimination and attained the CRC value of 0.632.…”

Section: Discussionmentioning

confidence: 99%

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

Yedukondalu

Sharma

2023

Sensors

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Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

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“…It requires the ability to manipulate numbers, apply mathematical operations, and accurately derive results in one's mind. Different implementations of the MA test have been proposed in the literature [16]- [18]. The Pased Auditory Serial Addition Test (PASAT) presents to the user a series of single-digit numbers.…”

Section: A Stress Inducing Tasksmentioning

confidence: 99%

Stress Assessment for Augmented Reality Applications Based on Head Movement Features

Ferrarotti,

Baldoni,

Carli

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.

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Evolutionary inspired approach for mental stress detection using EEG signal

Cited by 53 publications

References 46 publications

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

A Method of Extracting and Identifying College Students’ Music Psychological Features Based on EEG Signals

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

Stress Assessment for Augmented Reality Applications Based on Head Movement Features

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