scholarly journals ERP Detection Based on Smoothness Priors

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Reza Boostani ◽  
Mokhtar Mohammadi ◽  
Saeid Sanei
Keyword(s):  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Hypothesis Test ◽  
Real Life ◽  
Event Related Potentials ◽  
Training Data ◽  
Ratio Test ◽  
Related Potentials ◽  
Eeg Recordings ◽  
Objective Detection

<div>Abstract—Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in poststimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method outperforms the competing algorithms and improves the classification accuracy. Conclusion: The proposed SGLRT could be employed as a powerful means for different ERP detection schemes. Significance: ERP-based systems (e.g. brain-machine interfaces) mainly suffer from lack of classification accuracy, hence the proposed method is an important step toward real-life applicability of these systems.</div>

scholarly journals ERP Detection Based on Smoothness Priors

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Reza Boostani ◽  
Mokhtar Mohammadi ◽  
Saeid Sanei
Keyword(s):  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Hypothesis Test ◽  
Real Life ◽  
Event Related Potentials ◽  
Training Data ◽  
Ratio Test ◽  
Related Potentials ◽  
Eeg Recordings ◽  
Objective Detection

<div>Abstract—Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in poststimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method outperforms the competing algorithms and improves the classification accuracy. Conclusion: The proposed SGLRT could be employed as a powerful means for different ERP detection schemes. Significance: ERP-based systems (e.g. brain-machine interfaces) mainly suffer from lack of classification accuracy, hence the proposed method is an important step toward real-life applicability of these systems.</div>

scholarly journals A P300 Brain-Computer Interface With a Reduced Visual Field

2020 ◽  
Vol 14 ◽  
Author(s):  
Luiza Kirasirova ◽  
Vladimir Bulanov ◽  
Alexei Ossadtchi ◽  
Alexander Kolsanov ◽  
Vasily Pyatin ◽  
...  
Keyword(s):  
Visual Field ◽  
Visual Processing ◽  
Classification Accuracy ◽  
Peripheral Vision ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Central Visual Field ◽  
P300 Speller ◽  
Related Potentials

A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.

scholarly journals Artifact Removing From Eeg Recordings Using Independent Component Analysis With High-order Statistics

2021 ◽  
Vol 15 ◽  
pp. 1336-1345
Author(s):  
Theodor D. Popescu
Keyword(s):  
Eye Movements ◽  
Independent Component Analysis ◽  
Order Statistics ◽  
Event Related Potentials ◽  
Component Analysis ◽  
High Order ◽  
Dependent Component ◽  
Related Potentials ◽  
Eeg Recordings ◽  
High Order Statistics

Many methods have been proposed to remove artifacts from EEG recordings es- pecially those arising from eye movements and blinks. Often regression in time and frequency domain on parallel EEG and electrooculographic recordings is used, but this approach can become problematic in some cases. Use of Principal Com- ponent Analysis (PCA) has been proposed to re- move eye artifacts from multichannel EEG. This method is not effective when the activations from cerebral activity and artifacts have comparable amplitudes. In this paper it is presented a gener- ally applicable method for removing a wide vari- ety of artifacts from EEG recordings based on In- dependent Component Analysis (ICA) with high- order statistics. The method is applied with good results in the analysis of a sample lowpass event -related potentials (ERP) data.

Effects of detection and classification of resistive and elastic loads on endogenous event-related potentials

1994 ◽  
Vol 77 (3) ◽  
pp. 1246-1255 ◽  
Author(s):  
E. Bloch-Salisbury ◽  
A. Harver
Keyword(s):  
Classification Accuracy ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
The Other ◽  
Inspiratory Pressure ◽  
Button Press ◽  
Related Potentials ◽  
Load Types ◽  
Comparable Magnitude

Resistive and elastic loads added to inspiration are readily detected, and detection latencies vary as a function of load magnitude and load type. In the present study, we recorded endogenous event-related potentials (i.e., N2 and P3) to the detection and classification of large (15.0 cmH2O.1–1.s and 70.0 cmH2O/l) and small (1.45 cmH2O.1–1.s and 19.0 cmH2O/l) loads equated for subjective magnitude in 14 men (mean age 21.14 yr). In blocks of trials comprised of either large or small loads, subjects made a button-press response upon detecting a load and then classified the load as resistive or elastic. Loads were presented briefly (for approximately 200 ms) early in inspiration and at the same level of inspiratory pressure. For loads of comparable magnitude, subjects detected equivalent numbers of resistive and elastic loads but could not discriminate reliably between load types. On the other hand, the latency of N2 was shorter to larger than to smaller loads, to resistive than to elastic loads, and to correct than to incorrect load classifications. The latency of P3 was affected similarly by load magnitude and load type. These findings demonstrate that event-related potentials are elicited by brief presentations of resistive and elastic loads and that N2 and P3 latencies vary reliably as a function of load magnitude and load type. Most importantly, event-related potential latencies are sensitive to load type and to classification accuracy even when resistive and elastic loads are not distinguishable subjectively.

scholarly journals Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2842 ◽  
Author(s):  
Sen Wang ◽  
Qinglong Bao ◽  
Zengping Chen
Keyword(s):  
Target Tracking ◽  
Detection Probability ◽  
Sequential Monte Carlo ◽  
Signal To Noise Ratio ◽  
Hypothesis Test ◽  
Heuristic Method ◽  
False Alarms ◽  
Ratio Test ◽  
Probability Hypothesis Density ◽  
Multi Target Tracking

Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.

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.

scholarly journals EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Alejandro Gonzalez ◽  
Isao Nambu ◽  
Haruhide Hokari ◽  
Yasuhiro Wada
Keyword(s):  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Event Related Potentials ◽  
Binary Particle Swarm Optimization ◽  
Fisher Discriminant Analysis ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Related Potentials

Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.

Sign in / Sign up

Export Citation Format

Share Document