scholarly journals Preparatory Experiments Regarding Human Brain Perception and Reasoning of Image Complexity for Synthetic Color Fractal and Natural Texture Images via EEG

2020 ◽  
Vol 11 (1) ◽  
pp. 164
Author(s):  
Irina E. Nicolae ◽  
Mihai Ivanovici
Keyword(s):  
Human Perception ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Subjective Perception ◽  
Image Perception ◽  
Image Complexity ◽  
Temporal Area ◽  
Practical Applications ◽  
Related Potentials ◽  
Texture Complexity

Texture plays an important role in computer vision in expressing the characteristics of a surface. Texture complexity evaluation is important for relying not only on the mathematical properties of the digital image, but also on human perception. Human subjective perception verbally expressed is relative in time, since it can be influenced by a variety of internal or external factors, such as: Mood, tiredness, stress, noise surroundings, and so on, while closely capturing the thought processes would be more straightforward to human reasoning and perception. With the long-term goal of designing more reliable measures of perception which relate to the internal human neural processes taking place when an image is perceived, we firstly performed an electroencephalography experiment with eight healthy participants during color textural perception of natural and fractal images followed by reasoning on their complexity degree, against single color reference images. Aiming at more practical applications for easy use, we tested this entire setting with a WiFi 6 channels electroencephalography (EEG) system. The EEG responses are investigated in the temporal, spectral and spatial domains in order to assess human texture complexity perception, in comparison with both textural types. As an objective reference, the properties of the color textural images are expressed by two common image complexity metrics: Color entropy and color fractal dimension. We observed in the temporal domain, higher Event Related Potentials (ERPs) for fractal image perception, followed by the natural and one color images perception. We report good discriminations between perceptions in the parietal area over time and differences in the temporal area regarding the frequency domain, having good classification performance.

scholarly journals Color Texture Image Complexity—EEG-Sensed Human Brain Perception vs. Computed Measures

2021 ◽  
Vol 11 (9) ◽  
pp. 4306
Author(s):  
Irina E. Nicolae ◽  
Mihai Ivanovici
Keyword(s):  
Subjective Evaluation ◽  
Event Related Potentials ◽  
Texture Image ◽  
Eeg Signals ◽  
Wireless System ◽  
Image Complexity ◽  
Practical Applications ◽  
Non Invasive ◽  
Brain Signals ◽  
Related Potentials

In practical applications, such as patient brain signals monitoring, a non-invasive recording system with fewer channels for an easy setup and a wireless connection for remotely monitor physiological signals will be beneficial. In this paper, we investigate the feasibility of using such a system in a visual perception scenario. We investigate the complexity perception of color natural and synthetic fractal texture images, by studying the correlations between four types of data: image complexity that is expressed by computed color entropy and color fractal dimension, human subjective evaluation by scoring, and the measured brain EEG responses via Event-Related Potentials. We report on the considerable correlation experimentally observed between the recorded EEG signals and image complexity while considering three complexity levels, as well on the use of an EEG wireless system with few channels for practical applications, with the corresponding electrodes placement in accordance with the type of neural activity recorded.

scholarly journals Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7198
Author(s):  
Juan David Chailloux Peguero ◽  
Omar Mendoza-Montoya ◽  
Javier M. Antelis
Keyword(s):  
Visual Stimulation ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Single Trial ◽  
Experimental Protocol ◽  
Training Time ◽  
Machine Learning Model ◽  
Visual Frame ◽  
Related Potentials ◽  
The Relationship

The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.

scholarly journals Neural correlates of metacognition across the adult lifespan

2021 ◽  
Author(s):  
Helen Overhoff ◽  
Yiu Hong Ko ◽  
Daniel Feuerriegel ◽  
Gereon R. Fink ◽  
Jutta Stahl ◽  
...  
Keyword(s):  
Error Detection ◽  
Event Related Potentials ◽  
Perceptual Task ◽  
Subjective Perception ◽  
Detection Rates ◽  
Decision Accuracy ◽  
Adult Lifespan ◽  
Age Related ◽  
Related Potentials ◽  
Metacognitive Accuracy

Metacognitive accuracy describes the degree of overlap between the subjective perception of one's decision accuracy (i.e., confidence) and objectively observed performance. With older age, the need for accurate metacognitive evaluation increases; however, error detection rates typically decrease. We investigated the effect of ageing on metacognitive accuracy using event-related potentials (ERPs) reflecting error detection and confidence: the error/correct negativity (Ne/c) and the error/correct positivity (Pe/c). Sixty-five healthy adults (20 to 76 years) completed a complex perceptual task and provided confidence ratings. We found that metacognitive accuracy declined with age beyond the expected decline in task performance, while the adaptive adjustment of behaviour was well preserved. Pe/c amplitudes varied by confidence rating, but they did not mirror the reduction in metacognitive accuracy. Ne/c amplitudes decreased with age except for high confidence correct responses. The results suggest that age-related difficulties in metacognitive evaluation could be related to an impaired integration of decision accuracy and confidence information processing. Ultimately, training the metacognitive evaluation of fundamental decisions in older adults might constitute a promising endeavour.

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.

scholarly journals Exploring EEG Characteristics to Identify Emotional Reactions under Videogame Scenarios

2021 ◽  
Vol 11 (3) ◽  
pp. 378
Author(s):  
Laura Martínez-Tejada ◽  
Alex Puertas-González ◽  
Natsue Yoshimura ◽  
Yasuharu Koike
Keyword(s):  
Frequency Domain ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Emotional Reactions ◽  
Strong Correlations ◽  
Theta Band ◽  
Beta Band ◽  
Emotional Information ◽  
Related Potentials ◽  
Arousal And Valence

In this article we present the study of electroencephalography (EEG) traits for emotion recognition process using a videogame as a stimuli tool, and considering two different kind of information related to emotions: arousal–valence self-assesses answers from participants, and game events that represented positive and negative emotional experiences under the videogame context. We performed a statistical analysis using Spearman’s correlation between the EEG traits and the emotional information. We found that EEG traits had strong correlation with arousal and valence scores; also, common EEG traits with strong correlations, belonged to the theta band of the central channels. Then, we implemented a regression algorithm with feature selection to predict arousal and valence scores using EEG traits. We achieved better result for arousal regression, than for valence regression. EEG traits selected for arousal and valence regression belonged to time domain (standard deviation, complexity, mobility, kurtosis, skewness), and frequency domain (power spectral density—PDS, and differential entropy—DE from theta, alpha, beta, gamma, and all EEG frequency spectrum). Addressing game events, we found that EEG traits related with the theta, alpha and beta band had strong correlations. In addition, distinctive event-related potentials where identified in the presence of both types of game events. Finally, we implemented a classification algorithm to discriminate between positive and negative events using EEG traits to identify emotional information. We obtained good classification performance using only two traits related with frequency domain on the theta band and on the full EEG spectrum.

scholarly journals Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis

2020 ◽  
Author(s):  
Jan Sosulski ◽  
Jan-Philipp Kemmer ◽  
Michael Tangermann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Covariance Matrix ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Feature Representation ◽  
Training Data ◽  
Linear Discriminant ◽  
Related Potentials

AbstractElectroencephalogram data used in the domain of brain–computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.

Perception: A Model Comprising Two Modes of Consciousness Addendum: Evidence Based on Event-Related Potentials and Brain Lesions

1983 ◽  
Vol 56 (1) ◽  
pp. 211-220 ◽  
Author(s):  
Carl G. Aurell
Keyword(s):  
Human Perception ◽  
Event Related Potentials ◽  
Brain Lesions ◽  
Evidence Based ◽  
Brain Potentials ◽  
Related Potentials

The model is described in a previous paper where evidence is restricted to psychological findings. In the present paper evidence is taken from physiological findings derived from experiences with event-related brain potentials and from studies of agnosia caused by brain lesions. The hypothesis that two separate modes of consciousness are involved in human perception has obtained increased credibility.

scholarly journals Neural Sensitivity to Natural Image Statistics Changes during Middle Childhood

2020 ◽  
Author(s):  
Benjamin Balas ◽  
Alyson Saville
Keyword(s):  
Middle Childhood ◽  
Visual Processing ◽  
Event Related Potentials ◽  
Developmental Trajectory ◽  
Natural Images ◽  
Natural Image ◽  
Natural Image Statistics ◽  
Image Perception ◽  
Image Statistics ◽  
Related Potentials

AbstractNatural images have lawful statistical properties that the adult visual system is sensitive to, both in terms of behavior and neural responses to natural images. The developmental trajectory of sensitivity to natural image statistics remains unclear, however. In behavioral tasks, children appear to slowly acquire adult-like sensitivity to natural image statistics during middle childhood (Ellemberg et al., 2012), but in other tasks, infants exhibit some sensitivity to deviations of natural image structure (Balas & Woods, 2014). Here, we used event-related potentials (ERPs) to examine how sensitivity to natural image statistics changes during childhood at distinct stages of visual processing (the P1 and N1 components). We asked children (5-10 years old) and adults to view natural texture images with either positive/negative contrast, and natural/synthetic texture appearance (Portilla & Simoncelli, 2000) to compare electrophysiological responses to images that did or did not violate natural statistics. We hypothesized that children may only acquire sensitivity to these deviations from natural texture appearance late in middle childhood. Counter to this hypothesis, we observed significant responses to unnatural contrast and texture statistics at the N1 in all age groups. At the P1, however, only young children exhibited sensitivity to contrast polarity. The latter effect suggests greater sensitivity earlier in development to some violations of natural image statistics. We discuss these results in terms of changing patterns of invariant texture processing during middle childhood and ongoing refinement of the representations supporting natural image perception.

APPLICATION OF FUNCTIONAL NEUROMARKERS FOR CONSTRUCTING PROTOCOLS OF NEUROTHERAPY AND MONITORING THE SUCCESS OF TREATMENT IN CHRONIC CROSSED TRANSCORTICAL SENSORY APHASIA: A CASE STUDY

2016 ◽  
Vol 14 (4) ◽  
pp. 389-400
Author(s):  
Marzena Chantsoulis ◽  
Paweł Półrola ◽  
Jolanta Góral-Półrola ◽  
Izabela Herman-Sucharska ◽  
Juri D. Kropotov ◽  
...  
Keyword(s):  
Right Hemisphere ◽  
Event Related Potentials ◽  
Moderate Degree ◽  
Temporal Area ◽  
Aphasia Therapy ◽  
Sensory Aphasia ◽  
Related Potentials ◽  
Hyper Sensitivity ◽  
The Right

Background. The goal of the study was threefold: 1) to evaluate QEEG/ERPs in­dexes of functional brain impairment after a stroke associated with chronic crossed transcortical sensory aphasia, 2) to construct a neu­rotherapy protocol to compensate for this functional damage, and 3) to assess the changes in the functional neuromarkers induced by the neurotherapy sessions. Case study. A 72-year-old, strongly right-handed woman with atrial fibrillation sud­denly developed cerebral embolism of the right middle cerebral artery. She was treated conservatively, and the left hemiparesis, and aphasia – in a moderate degree, consequently existed. A CT-scan showed a large infarct lesion partially parallel to Wernicke’s area. After one year of ineffective aphasia therapy we constructed an experimental neuro­therapy protocol (TMS combined with comprehensive aphasia therapy) on the basis of an assessment of the spontaneous QEEG and event-related potentials (ERPs) in the cued GO/NOGO. The patient was assessed before and after the neurotherapy sessions by the same methodology. Conclusions.It was found that before the TMS treatment the temporal area (T6) generates a strong P2 wave in response to visual stimulus indicating a hyper-sensitivity of the neurons located at temporal areas of the right hemisphere. This was connected with crossed transcortical sensory aphasia found within the aphasia profile in the Polish version of the Western Aphasia Battery (K-WAB). The TMS sessions reduced this hyper-sensitivity substantially. The patient speech returned to the norm, she was to return to social life.

Temporal Dynamics of Japanese Morphogram and Syllabogram Processing in the Left Basal Temporal Area Studied by Event-Related Potentials

2009 ◽  
Vol 26 (3) ◽  
pp. 160-166 ◽  
Author(s):  
Keiko Usui ◽  
Akio Ikeda ◽  
Takashi Nagamine ◽  
Masao Matsuhashi ◽  
Masako Kinoshita ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Event Related Potentials ◽  
Temporal Area ◽  
Related Potentials
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