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 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 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.

scholarly journals Evaluation of Measurement Sites for Noninvasive Blood Glucose Sensing with Near-Infrared Transmission Spectroscopy

1999 ◽  
Vol 45 (9) ◽  
pp. 1621-1627 ◽  
Author(s):  
Jason J Burmeister ◽  
Mark A Arnold
Keyword(s):  
Blood Glucose ◽  
Body Fat ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Glucose Sensing ◽  
Infrared Transmission ◽  
Signal To Noise ◽  
Lower Lip ◽  
Percentage Of Body Fat ◽  
Noise Ratio

Abstract Six putative measurement sites were evaluated for noninvasive sensing of blood glucose by first-overtone near-infrared spectroscopy. The cheek, lower lip, upper lip, nasal septum, tongue, and webbing tissue between the thumb and forefinger were examined. These sites were evaluated on the basis of their chemical and physical properties as they pertain to the noninvasive measurement of glucose. Critical features included the effective optical pathlength of aqueous material within the tissue and the percentage of body fat within the optical path. Aqueous optical paths of 5 mm are required to measure clinically relevant concentrations of glucose in the first-overtone region. All of the tested sites met this requirement. The percentage of body fat affects the signal-to-noise ratio of the measurement and must be minimized for reliable glucose sensing. The webbing tissue contains a considerable amount of fat tissue and is clearly the worse measurement site. All other sites possess substantially less fat, with the least amount of fat in tongue tissue. For this reason, the tongue provides spectra with the highest signal-to-noise ratio and is, therefore, the site of choice on the basis of spectral quality.

Bioapplications and biotechnologies of upconversion nanoparticle-based nanosensors

The Analyst ◽  
2016 ◽  
Vol 141 (12) ◽  
pp. 3601-3620 ◽  
Author(s):  
Chengli Wang ◽  
Xiaomin Li ◽  
Fan Zhang
Keyword(s):  
Optical Properties ◽  
High Resistance ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Upconversion Nanoparticles ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Auto Fluorescence ◽  
New Generation ◽  
Narrow Emission

Upconversion nanoparticles (UCNPs), which can emit ultraviolet/visible (UV/Vis) light under near-infrared (NIR) excitation, are regarded as a new generation of nanoprobes because of their unique optical properties, including a virtually zero auto-fluorescence background for the improved signal-to-noise ratio, narrow emission bandwidths and high resistance to photo-bleaching.

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