scholarly journals A lifespan comparison of the reliability, test-retest stability, and signal-to-noise ratio of event-related potentials assessed during performance monitoring

2012 ◽  
Vol 50 (1) ◽  
pp. 111-123 ◽  
Author(s):  
Dorothea Hämmerer ◽  
Shu-Chen Li ◽  
Manuel Völkle ◽  
Viktor Müller ◽  
Ulman Lindenberger
Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 556
Author(s):  
Yuri Yoshida ◽  
Takumi Kawana ◽  
Eiichi Hoshino ◽  
Yasuyo Minagawa ◽  
Norihisa Miki

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.


2007 ◽  
Vol 118 (3) ◽  
pp. 690-695 ◽  
Author(s):  
Sanne Boesveldt ◽  
Antje Haehner ◽  
Henk W. Berendse ◽  
Thomas Hummel

2002 ◽  
Vol 49 (1) ◽  
pp. 31-40 ◽  
Author(s):  
M.M. Rohde ◽  
S.L. BeMent ◽  
J.E. Huggins ◽  
S.P. Levine ◽  
R.K. Kushwaha ◽  
...  

2012 ◽  
Vol 50 (1) ◽  
pp. 13-21
Author(s):  
Ph. Rombaux ◽  
C. Huart ◽  
A. Mouraux

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.


Author(s):  
Luigi Bianchi ◽  
Chiara Liti ◽  
Giampaolo Liuzzi ◽  
Veronica Piccialli ◽  
Cecilia Salvatore

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.


2013 ◽  
Vol 23 (02) ◽  
pp. 1350006 ◽  
Author(s):  
FENGYU CONG ◽  
ANH-HUY PHAN ◽  
PIIA ASTIKAINEN ◽  
QIBIN ZHAO ◽  
QIANG WU ◽  
...  

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.


Author(s):  
David A. Grano ◽  
Kenneth H. Downing

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.


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