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

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.

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

Fractal and Fractional ◽

10.3390/fractalfract5040225 ◽

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.

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A comparison between power spectral density and network metrics: An EEG study

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2019.101760 ◽

2020 ◽

Vol 57 ◽

pp. 101760 ◽

Cited By ~ 4

Author(s):

Matteo Demuru ◽

Simone Maurizio La Cava ◽

Sara Maria Pani ◽

Matteo Fraschini

Keyword(s):

Spectral Density ◽

Power Spectral Density ◽

Power Spectral ◽

Network Metrics

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

Applied Sciences ◽

10.3390/app10217639 ◽

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.

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