Recurrence plot structures reflect motor-related EEG pattern

Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.

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Novel dry electrode EEG headbands for home use: Comparing performance and comfort

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3036 ◽

2020 ◽

Vol 6 (3) ◽

pp. 139-142

Author(s):

Jens Haueisen ◽

Patrique Fiedler ◽

Anna Bernhardt ◽

Ricardo Gonçalves ◽

Carlos Fonseca

Keyword(s):

Brain Activity ◽

Skin Impedance ◽

Signal Quality ◽

The Novel ◽

Application Time ◽

Dry Electrodes ◽

Power Spectral ◽

Dry Electrode ◽

Increasing Trend ◽

Eeg Recordings

AbstractMonitoring brain activity at home using electroencephalography (EEG) is an increasing trend for both medical and non-medical applications. Gel-based electrodes are not suitable due to the gel application requiring extensive preparation and cleaning support for the patient or user. Dry electrodes can be applied without prior preparation by the patient or user. We investigate and compare two dry electrode headbands for EEG acquisition: a novel hybrid dual-textile headband comprising multipin and multiwave electrodes and a neoprene-based headband comprising hydrogel and spidershaped electrodes. We compare the headbands and electrodes in terms of electrode-skin impedance, comfort, electrode offset potential and EEG signal quality. We did not observe considerable differences in the power spectral density of EEG recordings. However, the hydrogel electrodes showed considerably increased impedances and offset potentials, limiting their compatibility with many EEG amplifiers. The hydrogel and spider-shaped electrodes required increased adduction, resulting in a lower wearing comfort throughout the application time compared to the novel headband comprising multipin and multiwave electrodes.

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Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

10.1101/2021.01.13.426522 ◽

2021 ◽

Author(s):

Leonhard Waschke ◽

Thomas Donoghue ◽

Lorenz Fiedler ◽

Sydney Smith ◽

Douglas D. Garrett ◽

...

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Spectral Exponent ◽

Non Invasive ◽

Neural Processes ◽

Highly Sensitive ◽

Neural Populations ◽

Sensory Cortices ◽

Behavioural Performance ◽

Eeg Recordings

AbstractA hallmark of electrophysiological brain activity is its 1/f-like spectrum – power decreases with increasing frequency. The steepness of this “roll-off” is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first demonstrate that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general, anaesthesia-driven as well as specific, attention-driven changes in E:I balance. We then present results from an EEG experiment during which participants detected faint target stimuli in streams of simultaneously presented auditory and visual noise. EEG spectral exponents over auditory and visual sensory cortices tracked stimulus spectral exponents of the corresponding domain, while evoked responses remained unchanged. Crucially, the degree of this stimulus–brain spectral-exponent coupling was positively linked to behavioural performance. Our results highlight the relevance of neural 1/f-like activity and enable the study of neural processes previously thought to be inaccessible in non-invasive human recordings.

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Design of Brain Activity Measurement for Brain ECVT Data Acquisition System

International Journal of Innovative Research in Medical Science ◽

10.23958/ijirms/vol06-i10/1223 ◽

2021 ◽

Vol 6 (10) ◽

pp. 630-634

Author(s):

Rizki Edmi Edison ◽

Rohmadi Rohmadi ◽

Sra Harke Pratama ◽

Muhammad Fathul Ihsan ◽

Almusfi Saputra ◽

...

Keyword(s):

Signal Analysis ◽

Brain Activity ◽

Activity Measurement ◽

Imaging Method ◽

Capacitance Sensor ◽

Non Invasive ◽

Power Spectral ◽

Activity Measurements ◽

Electrical Capacitance Volume Tomography ◽

The Brain

Brain Electrical Capacitance Volume Tomography (ECVT) has been developing as an alternative non-invasive brain imaging method. In this study, brain ECVT consisting of two channels, namely a capacitance sensor, is investigated. As a comparison, EEG sensor is used to measure brain activity simultaneously with the brain ECVT. Brain activity measurements were carried out at the pre-frontal lobe of Fp1 and Fp2 locations. The resulting signal was processed by filtering method and Power Spectral Density (PSD). The result of signal analysis shows that the measurement between EEG and ECVT shows the same activity of the two modalities.

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nalysis of Impact of Alcohol on Brain's Activity

10.36939/ir.202201121120 ◽

2021 ◽

Author(s):

◽

Dharitri Tripathy

Keyword(s):

Open Source ◽

Programming Language ◽

Emotional Regulation ◽

Brain Activity ◽

Time Frequency ◽

Memory And Attention ◽

Non Invasive ◽

Monitoring Process ◽

Beta Power ◽

The Impact

Electroencephalography is an electrophysiological monitoring process to capture electrical activity on the scalp that has been shown to represent the macroscopic activity of the surface layer of the brain underneath. It is typically non-invasive, with the electrodes placed along the scalp. Computer programs in different programming language such as MATLAB, Python are used to simulate and study brain signals. This thesis focuses on utilizing Python, an open-source programming language to understand the impact of alcohol on one’s memory and attention and comparing them with non-alcoholic brain. To carry out this research, we are using open-source EEG data collected from alcoholic and non-alcoholic subjects subjected to visual stimuli. Experiments are carried out to observe spatial patterns related to both groups' brain activity and their association with different region of brain such as memory, attention, somatosensory, and emotional regulation regions. Besides the spatial pattern, we are also focusing to find source signals and their association with respect to attention region to understand the impact of alcohol on one’s attention function. Finally, the optimal sources based on optimal alpha and gamma rhythms are estimated. For these optimal source channels, we estimated time-frequency based spectrogram to understand the association of other band powers for both groups. Beta power activities from these spectrograms are analyzed for both groups to understand attention-deficit caused by alcohol consumption. By analyzing the results from the experiments can help us understand the impact of alcohol on one's brain's activity.

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Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

10.1101/2021.02.09.430184 ◽

2021 ◽

Author(s):

Neng-Tai Chiu ◽

Stephanie Huwiler ◽

M. Laura Ferster ◽

Walter Karlen ◽

Hau-Tieng Wu ◽

...

Keyword(s):

Single Channel ◽

Linear Time ◽

Brain Activity ◽

Automated Detection ◽

Artifact Detection ◽

Time Frequency ◽

Long Term Monitoring ◽

Eeg Recordings ◽

Term Monitoring

AbstractBrain activity recordings outside clinical or laboratory settings using mobile EEG systems have recently gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g. single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts gives the opportunity for future extraction of heart rate features without ECG measurement.

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When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI

Frontiers in Neuroscience ◽

10.3389/fnins.2021.636424 ◽

2021 ◽

Vol 15 ◽

Author(s):

Catriona L. Scrivener

Keyword(s):

Brain Function ◽

Statistical Power ◽

Signal To Noise Ratio ◽

Brain Activity ◽

Relevant Literature ◽

Simultaneous Recording ◽

Functional Magnetic Resonance ◽

Spatial And Temporal Scales ◽

Non Invasive ◽

Research Questions

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

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Validation of Soft Multipin Dry EEG Electrodes

Sensors ◽

10.3390/s21206827 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6827

Author(s):

Janne J.A. Heijs ◽

Ruben Jan Havelaar ◽

Patrique Fiedler ◽

Richard J.A. van van Wezel ◽

Tjitske Heida

Keyword(s):

Signal To Noise Ratio ◽

Brain Activity ◽

Auditory Evoked Potential ◽

Strong Correlations ◽

Time Frequency ◽

Dry Electrodes ◽

Eyes Open ◽

Similar Accuracy ◽

Eye Closure ◽

40 Hz

Current developments towards multipin, dry electrodes in electroencephalography (EEG) are promising for applications in non-laboratory environments. Dry electrodes do not require the application of conductive gel, which mostly confines the use of gel EEG systems to the laboratory environment. The aim of this study is to validate soft, multipin, dry EEG electrodes by comparing their performance to conventional gel EEG electrodes. Fifteen healthy volunteers performed three tasks, with a 32-channel gel EEG system and a 32-channel dry EEG system: the 40 Hz Auditory Steady-State Response (ASSR), the checkerboard paradigm, and an eyes open/closed task. Within-subject analyses were performed to compare the signal quality in the time, frequency, and spatial domains. The results showed strong similarities between the two systems in the time and frequency domains, with strong correlations of the visual (ρ = 0.89) and auditory evoked potential (ρ = 0.81), and moderate to strong correlations for the alpha band during eye closure (ρ = 0.81–0.86) and the 40 Hz-ASSR power (ρ = 0.66–0.72), respectively. However, delta and theta band power was significantly increased, and the signal-to-noise ratio was significantly decreased for the dry EEG system. Topographical distributions were comparable for both systems. Moreover, the application time of the dry EEG system was significantly shorter (8 min). It can be concluded that the soft, multipin dry EEG system can be used in brain activity research with similar accuracy as conventional gel electrodes.

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Models for preterm cortical development using non invasive clinical EEG

Translational Neuroscience ◽

10.1515/tnsci-2017-0029 ◽

2017 ◽

Vol 8 (1) ◽

Author(s):

Nora Vanessa de Camp ◽

Florian Hense ◽

Bernd Lecher ◽

Helmut Scheu ◽

Jürgen Bergeler

Keyword(s):

Developmental Stages ◽

Phase Locking ◽

Cortical Development ◽

Age Groups ◽

Model Systems ◽

Network Activity ◽

Non Invasive ◽

Power Spectral ◽

Eeg Recordings ◽

Phase Amplitude

AbstractThe objective of this study was to evaluate the piglet and the mouse as model systems for preterm cortical development. According to the clinical context, we used non invasive EEG recordings. As a prerequisite, we developed miniaturized Ag/AgCl electrodes for full band EEG recordings in mice and verified that Urethane had no effect on EEG band power. Since mice are born with a “preterm” brain, we evaluated three age groups: P0/P1, P3/P4 and P13/P14. Our aim was to identify EEG patterns in the somatosensory cortex which are distinguishable between developmental stages and represent a physiologic brain development. In mice, we were able to find clear differences between age groups with a simple power analysis of EEG bands and also for phase locking and power spectral density. Interhemispheric coherence between corresponding regions can only be seen in two week old mice. The canolty maps for piglets as well as for mice show a clear PAC (phase amplitude coupling) pattern during development. From our data it can be concluded that analytic tools relying on network activity, as for example PAC (phase amplitude coupling) are best suited to extract basic EEG patterns of cortical development across species.

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Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network

Sensors ◽

10.3390/s21093050 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3050

Author(s):

Arturo Martínez-Rodrigo ◽

Beatriz García-Martínez ◽

Álvaro Huerta ◽

Raúl Alcaraz

Keyword(s):

Learning Algorithm ◽

Negative Impact ◽

Brain Activity ◽

Classification Performance ◽

Emotional States ◽

Physical And Mental Health ◽

Eeg Signals ◽

Deep Learning Algorithm ◽

Power Spectral ◽

Eeg Recordings

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain’s behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information.

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Electrophysiological Correlates of Virtual-Reality Applications in the Rehabilitation Setting: New Perspectives for Stroke Patients

Electronics ◽

10.3390/electronics10070836 ◽

2021 ◽

Vol 10 (7) ◽

pp. 836

Author(s):

Francesco Arcuri ◽

Camillo Porcaro ◽

Irene Ciancarelli ◽

Paolo Tonin ◽

Antonio Cerasa

Keyword(s):

Virtual Reality ◽

Brain Plasticity ◽

Eeg Signal ◽

Stroke Patients ◽

Time Frequency ◽

Non Invasive ◽

Power Spectral ◽

Frequency Domains ◽

Multisensory Training ◽

Vr Training

Here we reviewed the last evidence on the application of electroencephalography (EEG) as a non-invasive and portable neuroimaging method useful to extract hallmarks of neuroplasticity induced by virtual reality (VR) rehabilitation approaches in stroke patients. In the neurorehabilitation context, VR training has been used extensively to hamper the effects of motor treatments on the stroke’s brain. The concept underlying VR therapy is to improve brain plasticity by engaging users in multisensory training. In this narrative review, we present the key concepts of VR protocols applied to the rehabilitation of stroke patients and critically discuss challenges of EEG signal when applied as endophenotype to extract neurophysiological markers. When VR technology was applied to magnify the effects of treatments on motor recovery, significant EEG-related neural improvements were detected in the primary motor circuit either in terms of power spectral density or as time-frequency domains.

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