Evaluating Alpha Relative Power of EEG Signal during Psychophysiological Activities in Salat

Khanam, Farzana; Rahman, Md. Asadur; Ahmad, Mohiuddin

doi:10.1109/iciset.2018.8745614

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…Then, we have estimated PSD values for those 10 channels. PSD value has been calculated based on the Welch method [41,42] with a window of 50% overlapping. From Fig.…”

Section: Resultsmentioning

confidence: 99%

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Saha

Rahman²,

Alam

et al. 2021

SN COMPUT. SCI.

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The extraction methodology of the significant features from the signals is one of the most important prerequisite steps for EEG signal classification. Common spatial pattern (CSP) is a widely used feature extraction method for EEG signal but with a lacking of failing to maintain discriminative features between classes in the time domain, and further as a consequence, ends up in inconvenience with erroneous output. To overcome the limitations of the convention CSP, this research work proposes a novel frequency domain CSP (FCSP) method for feature extraction. This method proposes to convert the time domain EEG signal to its power spectral density (PSD) so that the event-related variation can be found in the frequency domain. After that, the CSP method is applied to the PSD values of the selected channels to extract the variation based on the spatial pattern of the channels for the events. The output of this method helps to extract simple features from the FCSP-PSD data for the classification. The proposed method is applied to motor imagery data from BCI competition IV. To check the applicability of the proposed method, a complex environment was created considering the same lobe events such as combined left and right feet (Class#1) versus right-hand (Class#2) imagery movement. To compare the performance of the proposed work, the method is also applied to the conventional classification problem (left-hand vs right-hand imagery movement) and found very promising results of 91% accuracy on average.

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“…Then, we have estimated PSD values for those 10 channels. PSD value has been calculated based on the Welch method [41,42] with a window of 50% overlapping. From Fig.…”

Section: Resultsmentioning

confidence: 99%

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Saha

Rahman²,

Alam

et al. 2021

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For discrete time signal, the notation x(t) becomes x(n) where t=nT (T is sampling time interval and n is the sequence number). Therefore, for analyzing the frequency content of the discrete time signal, PSD is the Fourier transform of the auto-correlation function which can be represented as [5], means autocorrelation for the periodic signal. But for the Ergodic process,…”

Section: F Power Spectrum Density Estimation Using Welch Methodsmentioning

confidence: 99%

“…In this research work, AP is calculated from 1 to 100 Hz (50 Hz already filtered by notch filter). Due to the aforementioned consequences, the RP is calculated as [5,39],…”

Section: G Relative Power Index Estimationmentioning

confidence: 99%

“…The finer temporal resolution of EEG signal benefits scientific arena to exploit it in research and engineering. Recent developments [1][2][3][4][5][6][7] prove that different human activities can be recognized or classified by the EEG signal. Besides vast applications and implementations of EEG signal in the various fields (biomedical instrumentation, neuroscience, brain-computer interface, etc.…”

Section: Introductionmentioning

confidence: 99%

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EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

Rahman¹,

Rashid²,

Khanam³

et al. 2019

ijacsa

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Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.

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“…More detailed research on the mechanism of the effect of spiritual experience on cardiovascular health emphasized the positive relationship between cardiac function and spiritual well-being [ 3 8 ]. Spiritual health makes the parasympathetic overcome the autonomic nervous system [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A 10-Year Cardiovascular Risk in Adults with Different Levels of Spiritual Health: Tehran Lipid and Glucose Study

Amiri

Ghadirian

Parvin

et al. 2023

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Background: Previous studies have shown that spiritual experience may reduce cardiovascular disease (CVDs). However, little is known about the relationship between spiritual health and the gender-specific risk of CVDs in communities with different cultures.Methods: A total of 3249 individuals (53.7% female, 75.0% middle-aged) participated in the Tehran Lipid and Glucose Study (TLGS) from 2015 to 2017 were included. Based on the ACC/AHA pooled cohort equation, CVD risk over ten years was examined. Spiritual health was measured using a developed tool for measuring spiritual health in Muslim populations (SHIMA-48). Linear regression models were used to assess the association between spiritual health and ACC/AHA risk scores. The natural logarithm scale was calculated to consider the normal distribution hypothesis of the regression model. Results:The current results suggest a slight but significant increase in the mean of spiritual health in women compared to men in both cognitive/emotional and behavioral dimensions (P < 0.001). In both sexes, a higher prevalence of smoking was observed in participants with lower levels of spiritual health (P < 0.004). In men, compared to those with a low level of spiritual health (the first tertile), the logarithm of the ACC-AHA risk score was reduced by 0.11 (P = 0.004) and 0.18 (P < 0.001) for those in the second and third tertiles of spiritual health, respectively. This result may be attributed to higher cigarette smoking among the latter group. Similar results were not observed in women. Conclusions:Current results indicate a gender-specific association between spiritual health and cardiovascular disease risk. Our findings imply that promoting spiritual health can be considered an effective strategy in future preventive interventions, primarily by controlling the desire to smoke in men.

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Evaluating Alpha Relative Power of EEG Signal during Psychophysiological Activities in Salat

Cited by 12 publications

References 8 publications

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

A 10-Year Cardiovascular Risk in Adults with Different Levels of Spiritual Health: Tehran Lipid and Glucose Study

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