scholarly journals Fractal Methods and Power Spectral Density as Means to Explore EEG Patterns in Patients Undertaking Mental Tasks

2021 ◽  
Vol 5 (4) ◽  
pp. 225
Author(s):  
Carlos Alberto Valentim ◽  
Claudio Marcio Cassela Inacio ◽  
Sergio Adriani David
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Brain Activity ◽  
Time Domain Analysis ◽  
Relevant Information ◽  
Fluctuation Analysis ◽  
Power Spectral ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Mental Tasks

Brain electrical activity recorded as electroencephalogram data provides relevant information that can contribute to a better understanding of pathologies and human behaviour. This study explores extant electroencephalogram (EEG) signals in search of patterns that could differentiate subjects undertaking mental tasks and reveals insights on said data. We estimated the power spectral density of the signals and found that the subjects showed stronger gamma brain waves during activity while presenting alpha waves at rest. We also found that subjects who performed better in those tasks seemed to present less power density in high-frequency ranges, which could imply decreased brain activity during tasks. In a time-domain analysis, we used Hall–Wood and Robust–Genton estimators along with the Hurst exponent by means of a detrented fluctuation analysis and found that the first two fractal measures are capable of better differentiating signals between the rest and activity datasets. The statistical results indicated that the brain region corresponding to Fp channels might be more suitable for analysing EEG data from patients conducting arithmetic tasks. In summary, both frequency- and time-based methods employed in the study provided useful insights and should be preferably used together in EEG analysis.

scholarly journals Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

2020 ◽  
Vol 10 (21) ◽  
pp. 7639
Author(s):  
Md Junayed Hasan ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
Hyun-Kyun Choi ◽  
Dae-Seung Yoo ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Sleep Stage ◽  
Human Subjects ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Healthy Human ◽  
Power Spectral ◽  
Eeg Data

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

scholarly journals Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models

2019 ◽  
Vol 9 (2) ◽  
pp. 418-422
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Signal To Noise Ratio ◽  
Standard Technique ◽  
Research Paper ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Power Spectral ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.

scholarly journals A comparison between power spectral density and network metrics: an EEG study

2019 ◽  
Author(s):  
Matteo Demuru ◽  
Simone Maurizio La Cava ◽  
Sara Maria Pani ◽  
Matteo Fraschini
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Good Practice ◽  
Clustering Coefficient ◽  
Volume Conduction ◽  
Functional Correlation ◽  
Power Spectral ◽  
Eeg Data ◽  
Similar Information ◽  
Network Metrics

AbstractPower spectral density (PSD) and network analysis performed on functional correlation (FC) patterns represent two common approaches used to characterize Electroencephalographic (EEG) data. Despite the two approaches are widely used, their possible association may need more attention. To investigate this question, we performed a comparison between PSD and some widely used nodal network metrics (namely strength, clustering coefficient and betweenness centrality), using two different publicly available resting-state EEG datasets, both at scalp and source levels, employing four different FC methods (PLV, PLI, AEC and AECC). Here we show that the two approaches may provide similar information and that their correlation depends on the method used to estimate FC. In particular, our results show a strong correlation between PSD and nodal network metrics derived from FC methods (PLV and AEC) that do not limit the effects of volume conduction/signal leakage. The correlations are less relevant for more conservative FC methods (AECC). These findings suggest that the results derived from the two different approaches may be not independent and should not be treated as distinct analyses. We conclude that it may represent good practice to report the findings from the two approaches in conjunction to have a more comprehensive view of the results.

scholarly journals EEG power spectral density in locked-in and completely locked-in state patients: a longitudinal study

2020 ◽  
Author(s):  
Arianna Secco ◽  
Alessandro Tonin ◽  
Aygul Rana ◽  
Andres Jaramillo-Gonzalez ◽  
Majid Khalili-Ardali ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Relevant Information ◽  
Eeg Spectrum ◽  
Power Spectral ◽  
Significant Difference ◽  
Communication Ability ◽  
The Brain ◽  
Lateral Sclerosis

Abstract Persons with their eye closed and without any means of communication is said to be in a completely locked-in state (CLIS) while when they could still open their eyes actively or passively and have some means of communication are said to be in locked-in state (LIS). Two patients in CLIS without any means of communication, and one patient in the transition from LIS to CLIS with means of communication, who have Amyotrophic Lateral Sclerosis were followed at a regular interval for more than 1 year. During each visit, resting-state EEG was recorded before the brain–computer interface (BCI) based communication sessions. The resting-state EEG of the patients was analyzed to elucidate the evolution of their EEG spectrum over time with the disease’s progression to provide future BCI-research with the relevant information to classify changes in EEG evolution. Comparison of power spectral density (PSD) of these patients revealed a significant difference in the PSD’s of patients in CLIS without any means of communication and the patient in the transition from LIS to CLIS with means of communication. The EEG of patients without any means of communication is devoid of alpha, beta, and higher frequencies than the patient in transition who still had means of communication. The results show that the change in the EEG frequency spectrum may serve as an indicator of the communication ability of such patients.

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral
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