scholarly journals Assessment of chemosensory function using electroencephalographic techniques

2012 ◽  
Vol 50 (1) ◽  
pp. 13-21
Author(s):  
Ph. Rombaux ◽  
C. Huart ◽  
A. Mouraux
Keyword(s):  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Objective Evaluation ◽  
Stimulus Onset ◽  
Signal To Noise ◽  
Brain Responses ◽  
Related Potentials ◽  
Noise Ratio ◽  
Time Domain Averaging ◽  
Chemosensory Function

Electroencephalographic techniques are widely used to provide an objective evaluation of the chemosensory function and to explore neural mechanisms related to the processing of chemosensory events. The most popular technique to evaluate brain responses to chemosensory stimuli is across trial time-domain averaging to reveal chemosensory event-related potentials (CSERP) embedded within the ongoing EEG. Nevertheless, this technique has a poor signal-to-noise ratio and cancels out stimulus-induced changes in the EEG signal that are not strictly phased-locked to stimulus onset. The fact that consistent CSERP are not systematically identifiable in healthy subjects currently constitutes a major limitation to the use of this technique for the diagnosis of chemosensory dysfunction. In this review, we will review the different techniques related to the recording and identification of CSERP, discuss some of their limitations, and propose some novel signal processing methods which could be used to enhance the signal-to-noise ratio of chemosensory event-related brain responses.

scholarly journals Capturing Human Perceptual and Cognitive Activities via Event-Related Potentials Measured with Candle-Like Dry Microneedle Electrodes

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 556
Author(s):  
Yuri Yoshida ◽  
Takumi Kawana ◽  
Eiichi Hoshino ◽  
Yasuyo Minagawa ◽  
Norihisa Miki
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Cognitive Activity ◽  
Event Related Potentials ◽  
Signal To Noise ◽  
Skin Preparation ◽  
Auditory Oddball ◽  
Related Potentials ◽  
Noise Ratio ◽  
Noise Contamination

We demonstrate capture of event-related potentials (ERPs) using candle-like dry microneedle electrodes (CMEs). CMEs can record an electroencephalogram (EEG) even from hairy areas without any skin preparation, unlike conventional wet electrodes. In our previous research, we experimentally verified that CMEs can measure the spontaneous potential of EEG from the hairy occipital region without preparation with a signal-to-noise ratio as good as that of the conventional wet electrodes which require skin preparation. However, these results were based on frequency-based signals, which are relatively robust compared to noise contamination, and whether CMEs are sufficiently sensitive to capture finer signals remained unclear. Here, we first experimentally verified that CMEs can extract ERPs as good as conventional wet electrodes without preparation. In the auditory oddball tasks using pure tones, P300, which represent ERPs, was extracted with a signal-to-noise ratio as good as that of conventional wet electrodes. CMEs successfully captured perceptual activities. Then, we attempted to investigate cerebral cognitive activity using ERPs. In processing the vowel and prosody in auditory stimuli such as /itta/, /itte/, and /itta?/, laterality was observed that originated from the locations responsible for the process in near-infrared spectroscopy (NIRS) and magnetoencephalography experiments. We simultaneously measured ERPs with CMEs and NIRS in the oddball tasks using the three words. Laterality appeared in NIRS for six of 10 participants, although laterality was not clearly shown in the results, suggesting that EEGs have a limitation of poor spatial resolution. On the other hand, successful capturing of MMN and P300 using CMEs that do not require skin preparation may be readily applicable for real-time applications of human perceptual activities.

Signal-to-noise ratio of chemosensory event-related potentials

2007 ◽  
Vol 118 (3) ◽  
pp. 690-695 ◽  
Author(s):  
Sanne Boesveldt ◽  
Antje Haehner ◽  
Henk W. Berendse ◽  
Thomas Hummel
Keyword(s):  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Signal To Noise ◽  
Related Potentials ◽  
Noise Ratio

Quality estimation of subdurally recorded, event-related potentials based on signal-to-noise ratio

2002 ◽  
Vol 49 (1) ◽  
pp. 31-40 ◽  
Author(s):  
M.M. Rohde ◽  
S.L. BeMent ◽  
J.E. Huggins ◽  
S.P. Levine ◽  
R.K. Kushwaha ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Signal To Noise ◽  
Quality Estimation ◽  
Related Potentials ◽  
Noise Ratio

scholarly journals Improving P300 Speller performance by means of optimization and machine learning

2021 ◽  
Author(s):  
Luigi Bianchi ◽  
Chiara Liti ◽  
Giampaolo Liuzzi ◽  
Veronica Piccialli ◽  
Cecilia Salvatore
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Support Vector ◽  
Early Stopping ◽  
Signal To Noise ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Noise Ratio ◽  
Number Of Iterations

AbstractBrain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.

MULTI-DOMAIN FEATURE EXTRACTION FOR SMALL EVENT-RELATED POTENTIALS THROUGH NONNEGATIVE MULTI-WAY ARRAY DECOMPOSITION FROM LOW DENSE ARRAY EEG

2013 ◽  
Vol 23 (02) ◽  
pp. 1350006 ◽  
Author(s):  
FENGYU CONG ◽  
ANH-HUY PHAN ◽  
PIIA ASTIKAINEN ◽  
QIBIN ZHAO ◽  
QIANG WU ◽  
...  
Keyword(s):  
Mismatch Negativity ◽  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Dense Array ◽  
Tucker Decomposition ◽  
Signal To Noise ◽  
Related Potentials ◽  
Canonical Polyadic Decomposition

Non-negative Canonical Polyadic decomposition (NCPD) and non-negative Tucker decomposition (NTD) were compared for extracting the multi-domain feature of visual mismatch negativity (vMMN), a small event-related potential (ERP), for the cognitive research. Since signal-to-noise ratio in vMMN is low, NTD outperformed NCPD. Moreover, we proposed an approach to select the multi-domain feature of an ERP among all extracted features and discussed determination of numbers of extracted components in NCPD and NTD regarding the ERP context.

scholarly journals Countering Cross-Individual Variance in Event Related Potentials with Functional Profiling

2018 ◽  
Author(s):  
Abdulmajeed Alsufyani ◽  
Alexia Zoumpoulaki ◽  
Marco Filetti ◽  
Dirk P. Janssen ◽  
Howard Bowman
Keyword(s):  
Sensitivity And Specificity ◽  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Data Sets ◽  
List Type ◽  
Simple Method ◽  
Brain Responses ◽  
Eeg Data ◽  
Functional Profiling ◽  
Related Potentials

AbstractIn this study, a simple method called the Weight Template (WT) is proposed for classifying brain responses of individuals into deceiving and non-deceiving. The performance of our method was evaluated on a number of identity deception data sets and on artificial EEG data sets. A comparison was made with a standard method used to measure P3 presence, called Peak-to-Peak. In the real experimental data, the WT showed higher performance in terms of sensitivity and specificity. In the artificial EEG data, in ERPs with low Signal-to-Noise Ratio (SNR), the WT was more resistant to noise and provided more accurate measures.HighlightsWe propose a method for classifying ERPs of the P3 Concealed Information Tests.The new method is based on computing a weighted template (WT) for each individual.The WT is used to characterize the shape of each individual’s P3.The performance of the WT was compared with the standard Peak-to-Peak method.The WT showed higher performance in terms of sensitivity and specificity.

Non-Extra Pixel Interpolation

2020 ◽  
Vol 20 (04) ◽  
pp. 2050031
Author(s):  
Olivier Rukundo
Keyword(s):  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Destination Image ◽  
Linear Scaling ◽  
Signal To Noise ◽  
Triangular Function ◽  
Noise Ratio ◽  
Image Pixels ◽  
Array Output ◽  
Pixel Interpolation

A non-extra pixel interpolation NPI is introduced for efficient image upscaling purposes. The NPI algorithm uses extended-triangular and linear scaling functions to match the pixel coordinates. The triangular function uses a modulo-operator with only two variables representing image pixels and scaling ratio. Every two variables of the linear scaling function represent the source/destination image pixels and scaling ratio. The traditional ceil function is used to round off non-integer pixel coordinates. The circshift and padarray functions are used to circularly shift the elements in array output by [Formula: see text]-amount in each dimension and pad elements of the [Formula: see text]th columns/rows by g-padsize in the shifted array, respectively. The [Formula: see text], [Formula: see text] and [Formula: see text] values are determined with respect to integer scaling ratios by a vector of [Formula: see text]-elements. The Exactness, Peak Signal-to-Noise Ratio, Signal-to-Noise Ratio and Discrete Fourier Transform techniques were used for objective evaluation purposes. Experiments demonstrated comparable results as well as the need for further researches.

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