scholarly journals Validation of Soft Multipin Dry EEG Electrodes

Sensors ◽  
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.

scholarly journals Validating Poly(3,4-ethylene dioxythiophene) Polystyrene Sulfonate-Based Textile Electroencephalography Electrodes by a Textile-Based Head Phantom

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3629
Author(s):  
Granch Berhe Tseghai ◽  
Benny Malengier ◽  
Kinde Anlay Fante ◽  
Lieva Van Langenhove
Keyword(s):  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Human Mind ◽  
Electrode Impedance ◽  
Polystyrene Sulfonate ◽  
Dry Electrodes ◽  
Dry Electrode ◽  
Potential Alternative ◽  
Spectral Perturbation ◽  
Coated Cotton

It is important to go through a validation process when developing new electroencephalography (EEG) electrodes, but it is impossible to keep the human mind constant, making the process difficult. It is also very difficult to identify noise and signals as the input signal is unknown. In this work, we have validated textile-based EEG electrodes constructed from a poly(3,4-ethylene dioxythiophene) polystyrene sulfonate:/polydimethylsiloxane coated cotton fabric using a textile-based head phantom. The performance of the textile-based electrode has also been compared against a commercial dry electrode. The textile electrodes collected a signal to a smaller skin-to-electrode impedance (−18.9%) and a higher signal-to-noise ratio (+3.45%) than Ag/AgCl dry electrodes. From an EEGLAB, it was observed that the inter-trial coherence and event-related spectral perturbation graphs of the textile-based electrodes were identical to the Ag/AgCl electrodes. Thus, these textile-based electrodes can be a potential alternative to monitor brain activity.

Forty-hertz Midlatency Auditory Evoked Potential Activity Predicts Wakeful Response during Desflurane and Propofol Anesthesia in Volunteers 

1999 ◽  
Vol 91 (5) ◽  
pp. 1209-1209 ◽  
Author(s):  
Robert C. Dutton ◽  
Warren D. Smith ◽  
Ira J. Rampil ◽  
Ben S. Chortkoff ◽  
Edmond I Eger
Keyword(s):  
Steady State ◽  
Frequency Spectrum ◽  
Auditory Evoked Potentials ◽  
Evoked Potential ◽  
Auditory Evoked Potential ◽  
Correct Prediction ◽  
Time Frequency ◽  
Prediction Probability ◽  
End Tidal ◽  
40 Hz

Background Suppression of response to command commonly indicates unconsciousness and generally occurs at anesthetic concentrations that suppress or eliminate memory formation. The authors sought midlatency auditory evoked potential indices that successfully differentiated wakeful responsiveness and unconsciousness. Methods The authors correlated midlatency auditory evoked potential indices with anesthetic concentrations permitting and suppressing response in 22 volunteers anesthetized twice (5 days apart), with desflurane or propofol. They applied stepwise increases of 0.5 vol% end-tidal desflurane or 0.5 microg/ml target plasma concentration of propofol to achieve sedation levels just bracketing wakeful response. Midlatency auditory evoked potentials were recorded, and wakeful response was tested by asking volunteers to squeeze the investigator's hand. The authors measured latencies and amplitudes from raw waveforms and calculated indices from the frequency spectrum and the joint time-frequency spectrogram. They used prediction probability (PK) to rate midlatency auditory evoked potential indices and concentrations of end-tidal desflurane and arterial propofol for prediction of responsiveness. A PK value of 1.00 means perfect prediction and a PK of 0.50 means a correct prediction 50% of the time (e.g., by chance). Results The approximately 40-Hz power of the frequency spectrum predicted wakefulness better than all latency or amplitude indices, although not all differences were statistically significant. The PK values for approximately 40-Hz power were 0.96 during both desflurane and propofol anesthesia, whereas the PK values for the best-performing latency and amplitude index, latency of the Nb wave, were 0.86 and 0.88 during desflurane and propofol (P = 0.10 for -40-Hz power compared with Nb latency), and for the next highest, latency of the Pb wave, were 0.82 and 0.84 (P < 0.05). The performance of the best combination of amplitude and latency variables was nearly equal to that of approximately 40-Hz power. The approximately 40-Hz power did not provide a significantly better prediction than anesthetic concentration; the PK values for concentrations of desflurane and propofol were 0.91 and 0.94. Changes of 40-Hz power values of 20% (during desflurane) and 16% (during propofol) were associated with a change in probability of nonresponsiveness from 50% to 95%. Conclusions The approximately 40-Hz power index and the best combination of amplitude and latency variables perform as well as predictors of response to command during desflurane and propofol anesthesia as the steady-state concentrations of these anesthetic agents. Because clinical conditions may limit measurement of steady-state anesthetic concentrations, or comparable estimates of cerebral concentration, the approximately 40-Hz power could offer advantages for predicting wakeful responsiveness.

scholarly journals Recurrence plot structures reflect motor-related EEG pattern

2019 ◽  
pp. 282-286
Author(s):  
Elena Pitsik ◽  
Nikita Frolov
Keyword(s):  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Recurrence Plot ◽  
Mathematical Tool ◽  
Recurrence Plots ◽  
Time Frequency ◽  
Non Invasive ◽  
Power Spectral ◽  
Nonstationary Data ◽  
Eeg Recordings

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.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

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

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.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

scholarly journals Noise masking method based on an effective ratio mask estimation in Gammatone channels

2018 ◽  
Vol 7 ◽  
Author(s):  
Feng Bao ◽  
Waleed H. Abdulla
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Wiener Filter ◽  
Power Spectra ◽  
Auditory Scene Analysis ◽  
Accurate Estimation ◽  
Noise Power ◽  
Noise Masking ◽  
Time Frequency ◽  
Mask Estimation

In computational auditory scene analysis, the accurate estimation of binary mask or ratio mask plays a key role in noise masking. An inaccurate estimation often leads to some artifacts and temporal discontinuity in the synthesized speech. To overcome this problem, we propose a new ratio mask estimation method in terms of Wiener filtering in each Gammatone channel. In the reconstruction of Wiener filter, we utilize the relationship of the speech and noise power spectra in each Gammatone channel to build the objective function for the convex optimization of speech power. To improve the accuracy of estimation, the estimated ratio mask is further modified based on its adjacent time–frequency units, and then smoothed by interpolating with the estimated binary masks. The objective tests including the signal-to-noise ratio improvement, spectral distortion and intelligibility, and subjective listening test demonstrate the superiority of the proposed method compared with the reference methods.

scholarly journals Now you see it, now you don't: optimal parameters for interslice stimulation in concurrent TMS-fMRI

2021 ◽  
Author(s):  
Catriona L Scrivener ◽  
Jade B Jackson ◽  
Marta Morgado Correia ◽  
Marius Mada ◽  
Alexandra Woolgar
Keyword(s):  
Single Channel ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Surface Coil ◽  
Acquisition Time ◽  
Surface Coils ◽  
Human Participant ◽  
Set Up ◽  
Promising Avenue ◽  
Slice Acquisition

The powerful combination of transcranial magnetic stimulation (TMS) concurrent with functional magnetic resonance imaging (fMRI) provides rare insights into the causal relationships between brain activity and behaviour. Despite a recent resurgence in popularity, TMS-fMRI remains technically challenging. Here we examined the feasibility of applying TMS during short gaps between fMRI slices to avoid incurring artefacts in the fMRI data. We quantified signal dropout and changes in temporal signal-to-noise ratio (tSNR) for TMS pulses presented at timepoints from 100ms before to 100ms after slice onset. Up to 3 pulses were delivered per volume using MagVenture's MR-compatible TMS coil. We used a spherical phantom, two 7-channel TMS-dedicated surface coils, and a multiband (MB) sequence (factor=2) with interslice gaps of 100ms and 40ms, on a Siemens 3T Prisma-fit scanner. For comparison we repeated a subset of parameters with a more standard single-channel TxRx (birdcage) coil, and with a human participant and surface coil set up. We found that, even at 100% stimulator output, pulses applied at least -40ms/+50ms from the onset of slice readout avoid incurring artifacts. This was the case for all three setups. Thus, an interslice protocol can be achieved with a frequency of up to ~10 Hz, using a standard EPI sequence (slice acquisition time: 62.5ms, interslice gap: 40ms). Faster stimulation frequencies would require shorter slice acquisition times, for example using in-plane acceleration. Interslice TMS-fMRI protocols provide a promising avenue for retaining flexible timing of stimulus delivery without incurring TMS artifacts.

scholarly journals Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

2021 ◽  
Vol 7 ◽  
pp. e638
Author(s):  
Md Nahidul Islam ◽  
Norizam Sulaiman ◽  
Fahmid Al Farid ◽  
Jia Uddin ◽  
Salem A. Alyami ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Network Architecture ◽  
Auditory Evoked Potential ◽  
Human Communication ◽  
Time Frequency ◽  
The Neural Network ◽  
Cortex Area ◽  
Wide Range ◽  
Functional Reliability ◽  
Fully Connected

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.

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