Identification the limit of conscious perception of surface EEG signals with visual evoked potentials

2012 ◽  
Author(s):  
J. F. Sarmiento ◽  
A. B. Benevides ◽  
D. Romero ◽  
T. F. Bastos ◽  
M. Zanotti ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Conscious Perception ◽  
Eeg Signals ◽  
Surface Eeg ◽  
Visual Evoked

Experimental evidences for visual evoked potentials with stimuli beyond the conscious perception threshold

2011 ◽  
Author(s):  
Sergio G. Ramos-Junior ◽  
Daniel R. Celino ◽  
Fauzi F. Rodor ◽  
Moises R.N. Ribeiro ◽  
Sandra M.T. Muller ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Conscious Perception ◽  
Perception Threshold ◽  
Visual Evoked

scholarly journals Evaluation of Wigner-Ville Distribution Features to Estimate Steady-State Visual Evoked Potentials' Stimulation Frequency

2021 ◽  
pp. 133-136
Author(s):  
Murside Degirmenci ◽  
Ebru Sayilgan ◽  
Yalcin Isler
Keyword(s):  
Machine Learning ◽  
Steady State ◽  
Evoked Potentials ◽  
Ensemble Learning ◽  
Visual Evoked Potentials ◽  
Support Vector ◽  
Eeg Signals ◽  
Data Set ◽  
Time Frequency ◽  
Visual Evoked

Brain Computer Interface (BCI) is a system that enables people to communicate with the outside world and control various electronic devices by interpreting only brain activity (motor movement imagination, emotional state, any focused visual or auditory stimulus, etc.). The visual stimulation based recording is one of the most popular methods among various electroencephalography (EEG) recording methods. Steady-state visual-evoked potentials (SSVEPs) where visual objects are blinking at a fixed frequency play an important role due to their high signal-to-noise ratio and higher information transfer rate in BCI applications. However, the design of multiple (more than 3) commands systems in SSVEPs based BCI systems is limited. The different approaches are recommended to overcome these problems. In this study, an approach based on machine learning is proposed to determine stimulating frequency in SSVEP signals. The data set (AVI SSVEP Dataset) is obtained through open access from the internet for simulations. The dataset includes EEG signals that was recorded when subjects looked at a flickering frequency at seven different frequencies (6-6.5-7-7.5-8.2-9.3-10Hz). In the machine learning-based approach Wigner-Ville Distribution (WVD) is used and features are extracted using Time-Frequency (TF) representations of EEG signals. These features are classified by Decision Tree, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes, Ensemble Learning classifiers. Simulation results demonstrate that the proposed approach achieved promising accuracy rates for 7 command SSVEP systems. As a consequence, the maximum accuracy is achieved in the Ensemble Learning classifier with 47.60%.

Relationship of Visual-Evoked Potentials With Specific Symptoms of Schizotypy

2012 ◽  
Author(s):  
Jeffrey S. Bedwell ◽  
Yuri Rassovsky ◽  
Pamela Butler ◽  
Andrea Ranieri ◽  
Christopher Spencer ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Relationship Of ◽  
Specific Symptoms ◽  
Visual Evoked
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