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.

scholarly journals The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Di ◽  
Xingwei An ◽  
Wenxiao Zhong ◽  
Shuang Liu ◽  
Dong Ming
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Individual Identification ◽  
Frequency Range ◽  
Cross Spectrum ◽  
Power Spectral ◽  
Phase Lags ◽  
Individual Features

An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85–100% for intra-experiment dataset, and were 80–100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8–40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

The Comparisons of Heart Rate Variability and Perceived Exertion During Simulated Cycling with Various Viewing Devices

2008 ◽  
Vol 17 (6) ◽  
pp. 575-583 ◽  
Author(s):  
Shih-Fong Huang ◽  
Po-Yi Tsai ◽  
Wen-Hsu Sung ◽  
Chih-Yung Lin ◽  
Tien-Yow Chuang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Perceived Exertion ◽  
Low Frequency ◽  
Control Group ◽  
Threshold Intensity ◽  
Power Spectral ◽  
Significant Difference

Sympathovagal modulation during immersion in a virtual environment is an important influence on human performance of a task. The aim of this study is to investigate sympathovagal modulation using heart rate variability and perceived exertion during exercise in a virtual reality (VR) environment. Sixteen young healthy volunteers were tested while using a stationary bicycle and maintained at an anaerobic threshold intensity for exercise sessions of approximately 10 min duration. Four randomized viewing alternatives were provided including desktop monitor, projector, head mounted device (HMD), and no simulation display. The “no simulation display” served as the control group. A quick ramp exercise test was conducted and maintained at an anaerobic threshold intensity for each session to evaluate power spectral density and rating of perceived exertion (RPE). The sampled heart rate data were rearranged by cubic spline interpolation into power spectrums spanning the ultra-low frequency (ULF) to high frequency (HF) range. A significant difference was found between the no-display and projector groups for total power (TP) and very low frequency (VLF) components. In particular, there was a significant difference when comparing HMD and no-display exercise RPE curves within 6 min of cycling and at the termination of the exercise. A significant difference was also achieved in projector vs. control group comparison at the termination of the exercise. Our results indicate that the use of HMD and the projected VR during cycling can reduce the TP and VLF power spectral density through a proposed decrease in the renin-angiotensin system, with the implication that this humoral effect may enable anaerobic exercise for longer durations through a reduction in sympathetic tone and subsequent increased blood flow to the muscles.

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.

Increased power spectral density in resting-state pain-related brain networks in fibromyalgia

Pain ◽  
2013 ◽  
Vol 154 (9) ◽  
pp. 1792-1797 ◽  
Author(s):  
Ji-Young Kim ◽  
Seong-Ho Kim ◽  
Jeehye Seo ◽  
Sang-Hyon Kim ◽  
Seung Woo Han ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Brain Networks ◽  
Power Spectral

scholarly journals Power Spectral Density and Functional Connectivity Changes due to a Sensorimotor Neurofeedback Training: A Preliminary Study

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Juan L. Terrasa ◽  
Guzmán Alba ◽  
Ignacio Cifre ◽  
Beatriz Rey ◽  
Pedro Montoya ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Resting State ◽  
Performance Enhancement ◽  
Neurofeedback Training ◽  
Functional Changes ◽  
Power Spectral ◽  
Before And After ◽  
Exploratory Procedure

Neurofeedback is a form of neuromodulation based on learning to modify some aspects of cortical activity. Sensorimotor rhythm (SMR) oscillation is one of the most used frequency bands in neurofeedback. Several studies have shown that subjects can learn to modulate SMR power to control output devices, but little is known about possible related changes in brain networks. The aim of this study was to investigate the enhanced performance and changes in EEG power spectral density at somatosensory cerebral areas due to a bidirectional modulation-based SMR neurofeedback training. Furthermore, we also analyzed the functional changes in somatosensory areas during resting state induced by the training as exploratory procedure. A six-session neurofeedback protocol based on learning to synchronize and desynchronize (modulate) the SMR was implemented. Moreover, half of the participants were enrolled in two functional magnetic resonance imaging resting-state sessions (before and after the training). At the end of the training, participants showed a successful performance enhancement, an increase in SMR power specific to somatosensory locations, and higher functional connectivity between areas associated with somatosensory activity in resting state. Our research increases the better understanding of the relation between EEG neuromodulation and functional changes and the use of SMR training in clinical practice.

The Characteristics of Power Spectral Density in Bipolar Disorder at the Resting State

2021 ◽  
pp. 155005942110504
Author(s):  
Masakazu Sunaga ◽  
Yuichi Takei ◽  
Yutaka Kato ◽  
Minami Tagawa ◽  
Tomohiro Suto ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Frequency Band ◽  
Resting State ◽  
Anterior Cingulate ◽  
Alpha Band ◽  
Default Mode ◽  
Power Spectral ◽  
High Beta

Bipolar disorder (BD) is a common psychiatric disorder, but its pathophysiology is not fully elucidated. The current study focused on its electrophysiological characteristics, especially power spectral density (PSD). Resting state with eyes opened magnetoencephalography data were collected from 21 patients with BD and 22 healthy controls. The whole brain's PSD was calculated from source reconstructed waveforms at each frequency band (delta: 1-3 Hz, theta: 4-7 Hz, alpha: 8-12 Hz, low beta: 13-19 Hz, high beta: 20-29 Hz, and gamma: 30-80 Hz). We compared PSD values on the marked vertices at each frequency band between healthy and patient groups using a Mann-Whitney rank test to examine the relationship between significantly different PSD and clinical measures. The PSD in patients with BD was significantly decreased in lower frequency bands, mainly in the default mode network (DMN) areas (bilateral medial prefrontal cortex, bilateral precuneus, left inferior parietal lobe, and right temporal cortex in the alpha band) and salience network areas (SAL; left anterior insula [AI] at the delta band, anterior cingulate cortex at the theta band, and right AI at the alpha band). No significant differences in PSD were observed at low beta and high beta. PSD was not correlated with age or other clinical scales. Altered PSDs of the DMN and SAL were observed in the delta, theta, and alpha bands. These alterations contribute to the vulnerability of BD through the disturbance of self-referential mental activity and switching between the default mode and frontoparietal networks.

scholarly journals Effect of Pilates Training on Alpha Rhythm

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Zhijie Bian ◽  
Hongmin Sun ◽  
Chengbiao Lu ◽  
Li Yao ◽  
Shengyong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Brain Function ◽  
Alpha Rhythm ◽  
Degenerative Diseases ◽  
Global Synchronization ◽  
The Neural Network ◽  
Power Spectral ◽  
The Brain

In this study, the effect of Pilates training on the brain function was investigated through five case studies. Alpha rhythm changes during the Pilates training over the different regions and the whole brain were mainly analyzed, including power spectral density and global synchronization index (GSI). It was found that the neural network of the brain was more active, and the synchronization strength reduced in the frontal and temporal regions due to the Pilates training. These results supported that the Pilates training is very beneficial for improving brain function or intelligence. These findings maybe give us some line evidence to suggest that the Pilates training is very helpful for the intervention of brain degenerative diseases and cogitative dysfunction rehabilitation.

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