Analysis of EEG Signals and Biomedical Changes Due to Meditation on Brain by Using ICA for Feature Extraction

Korde, Kajal S.; Paikrao, Prashant; Jadhav, Narendra

doi:10.1109/iccons.2018.8663129

Cited by 15 publications

(6 citation statements)

References 8 publications

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“…Hou et al [ 11 ] found that combining statistical features with fractal dimension and power features improved the classification accuracy of stress. Moreover, study in [ 103 ] found that the variance values are higher in rest than stress levels, whereas kurtosis showed increased values in stress conditions when moving from delta to gamma bands. On the other hand, the main drawback is related to using all these features in stress estimation, which leads to longer time processing.…”

Section: Eeg Analysis Methodsmentioning

confidence: 99%

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Sensors

103

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Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contains rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Accordingly, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

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Section: Eeg Analysis Methodsmentioning

confidence: 99%

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Sensors

103

View full text Add to dashboard Cite

show abstract

“…(4) EEG monitors cognitive-affective processing in the absence of behavioral responses [11]. In terms of frequency, we all have five types of brainwave (Gamma, Beta, Alpha, Theta, Delta) and ''each frequency is measured in cycles per second (Hz) and has its own set of characteristics representing a specific level of brain activity and a unique state of consciousness'' [12]. This is represented in Table 2.…”

Section: Biometricsmentioning

confidence: 99%

SaS-BCI: a new strategy to predict image memorability and use mental imagery as a brain-based biometric authentication

Yousefi

Kolivand

Baker

2020

Neural Comput & Applic

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Security authentication is one of the most important levels of information security. Nowadays, human biometric techniques are the most secure methods for authentication purposes that cover the problems of older types of authentication like passwords and pins. There are many advantages of recent biometrics in terms of security; however, they still have some disadvantages. Progresses in technology made some specific devices, which make it possible to copy and make a fake human biometric because they are all visible and touchable. According to this matter, there is a need for a new biometric to cover the issues of other types. Brainwave is human data, which uses them as a new type of security authentication that has engaged many researchers. There are some research and experiments, which are investigating and testing EEG signals to find the uniqueness of human brainwave. Some researchers achieved high accuracy rates in this area by applying different signal acquisition techniques, feature extraction and classifications using Brain–Computer Interface (BCI). One of the important parts of any BCI processes is the way that brainwaves could be acquired and recorded. A new Signal Acquisition Strategy is presented in this paper for the process of authorization and authentication of brain signals specifically. This is to predict image memorability from the user’s brain to use mental imagery as a visualization pattern for security authentication. Therefore, users can authenticate themselves with visualizing a specific picture in their minds. In conclusion, we can see that brainwaves can be different according to the mental tasks, which it would make it harder using them for authentication process. There are many signal acquisition strategies and signal processing for brain-based authentication that by using the right methods, a higher level of accuracy rate could be achieved which is suitable for using brain signal as another biometric security authentication.

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“…Hou et al [11] found that combining statistical features with fractal dimension and power features improved the classification accuracy of stress. Moreover, study in [86] saw that the variance values are higher in rest than stress levels, whereas kurtosis showed increased values in stress conditions when moving from delta to gamma bands (increased EEG frequency). On the other hand, the main drawback is related to using all these features in stress estimation, which leads to longer time processing.…”

Section: Statistical Featuresmentioning

confidence: 99%

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Preprint

View full text Add to dashboard Cite

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

show abstract

Analysis of EEG Signals and Biomedical Changes Due to Meditation on Brain by Using ICA for Feature Extraction

Cited by 15 publications

References 8 publications

A Review on Mental Stress Assessment Methods Using EEG Signals

A Review on Mental Stress Assessment Methods Using EEG Signals

SaS-BCI: a new strategy to predict image memorability and use mental imagery as a brain-based biometric authentication

A Review on Mental Stress Assessment Methods Using EEG Signals

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