Real-world structure facilitates the rapid emergence of scene category information in visual brain signals

Natural scenes are structured, with different types of information appearing in predictable locations. Here, we use EEG decoding to show that the visual brain uses this structure to efficiently analyze scene content. During early visual processing, the category of a scene (e.g., a church vs. a supermarket) could be more accurately decoded from EEG signals when the scene adhered to its typical spatial structure compared with when it did not.

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Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based on Wavelet Denoising Technique

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.23.9 ◽

2015 ◽

Vol 23 ◽

pp. 9-17 ◽

Cited By ~ 4

Author(s):

Chao Zhu Zhang ◽

Ahmed Kareem Abdullah

Keyword(s):

Independent Component Analysis ◽

Blind Source Separation ◽

Signal Analysis ◽

Reference Signal ◽

Wavelet Denoising ◽

Eeg Signals ◽

Neural Signal ◽

Brain Signals ◽

Recording Process ◽

Different Types

Several problems in EEG-brain signal analysis are not solved, such as presence of an artifact during the recording process, particularly the eye artifact (ElectroOculoGram (EOG)) which makes the analysis of EEG-brain signals very difficult. Blind source separation technique is one of the important techniques used to clean the EEG signals from different types of artifacts. Independent component analysis (ICA) techniques are widely used for this purpose, but unfortunately the ICA techniques have inherent shortcoming such as source ambiguity and unordered components. Therefore, the researchers used ICA-Reference algorithm. The main problem in ICA-Reference algorithm is to find clean reference signal to extract the wanted signal. Recently, many algorithms proposed to generate the artifact reference, but unfortunately, clean artifact signal not satisfied. In this paper wavelet denoising technique is used to solve this problem by decompose the artifact reference signal into pure artifact signal and residual neural signal. The proposed algorithm used frontal channels instead of EOG channels to extract the EOG reference signal.

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Differential Tuning of Ventral and Dorsal Streams during the Generation of Common and Uncommon Tool Uses

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01161 ◽

2017 ◽

Vol 29 (11) ◽

pp. 1791-1802 ◽

Cited By ~ 6

Author(s):

Heath E. Matheson ◽

Laurel J. Buxbaum ◽

Sharon L. Thompson-Schill

Keyword(s):

Tool Use ◽

Visual Information ◽

Behavioral Measures ◽

Category Information ◽

Categorical Information ◽

Different Types ◽

Differential Tuning ◽

The Common ◽

Types Of Information ◽

Cortical Representations

Our use of tools is situated in different contexts. Prior evidence suggests that diverse regions within the ventral and dorsal streams represent information supporting common tool use. However, given the flexibility of object concepts, these regions may be tuned to different types of information when generating novel or uncommon uses of tools. To investigate this, we collected fMRI data from participants who reported common or uncommon tool uses in response to visually presented familiar objects. We performed a pattern dissimilarity analysis in which we correlated cortical patterns with behavioral measures of visual, action, and category information. The results showed that evoked cortical patterns within the dorsal tool use network reflected action and visual information to a greater extent in the uncommon use group, whereas evoked neural patterns within the ventral tool use network reflected categorical information more strongly in the common use group. These results reveal the flexibility of cortical representations of tool use and the situated nature of cortical representations more generally.

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Real-world structure facilitates the rapid emergence of scene category information in visual brain signals

10.1101/2020.03.24.004937 ◽

2020 ◽

Cited By ~ 1

Author(s):

Daniel Kaiser ◽

Greta Häberle ◽

Radoslaw M. Cichy

Keyword(s):

Visual System ◽

Real World ◽

Visual Processing ◽

Relevant Information ◽

Natural Environments ◽

Multivariate Pattern Analysis ◽

Categorical Analysis ◽

Category Information ◽

Brain Signals ◽

Multivariate Pattern

AbstractIn everyday life, our visual surroundings are not arranged randomly, but structured in predictable ways. Although previous studies have shown that the visual system is sensitive to such structural regularities, it remains unclear whether the presence of an intact structure in a scene also facilitates the cortical analysis of the scene’s categorical content. To address this question, we conducted an EEG experiment during which participants viewed natural scene images that were either “intact” (with their quadrants arranged in typical positions) or “jumbled” (with their quadrants arranged into atypical positions). We then used multivariate pattern analysis to decode the scenes’ category from the EEG signals (e.g., whether the participant had seen a church or a supermarket). The category of intact scenes could be decoded rapidly within the first 100ms of visual processing. Critically, within 200ms of processing category decoding was more pronounced for the intact scenes compared to the jumbled scenes, suggesting that the presence of real-world structure facilitates the extraction of scene category information. No such effect was found when the scenes were presented upside-down, indicating that the facilitation of neural category information is indeed linked to a scene’s adherence to typical real-world structure, rather than to differences in visual features between intact and jumbled scenes. Our results demonstrate that early stages of categorical analysis in the visual system exhibit tuning to the structure of the world that may facilitate the rapid extraction of behaviorally relevant information from rich natural environments.

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The Time Course of Abstract Visual Representation

Perception ◽

10.1068/p3396 ◽

2003 ◽

Vol 32 (5) ◽

pp. 579-592 ◽

Cited By ~ 52

Author(s):

Benjamin W Tatler ◽

Iain D Gilchrist ◽

Jenny Rusted

Keyword(s):

Visual System ◽

Time Course ◽

Change Blindness ◽

Natural Scenes ◽

Complex Information ◽

Abstract Representations ◽

Different Types ◽

Time Courses ◽

Types Of Information ◽

Pictorial Representations

Studies in change blindness re-enforce the suggestion that veridical, pictorial representations that survive multiple relocations of gaze are unlikely to be generated in the visual system. However, more abstract information may well be extracted and represented by the visual system. In this paper we study the types of information that are retained and the time courses over which these representations are constructed when participants view complex natural scenes. We find that such information is retained and that the resultant abstract representations encode a range of information. Different types of information are extracted and represented over different time courses. After several seconds of viewing natural scenes, our visual system is able to construct a complex information-rich representation.

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Analysis on Visual Signal based on the Effect of Eye Massaging Device using Wavelet Transform

Jurnal Teknologi ◽

10.11113/jt.v74.4664 ◽

2015 ◽

Vol 74 (6) ◽

Author(s):

Azmi Alwi ◽

Fatin Afiqa Mansor ◽

Rubita Sudirman

Keyword(s):

Wavelet Transform ◽

Visual Processing ◽

Visual Signal ◽

Eeg Signals ◽

Test Analysis ◽

Average Value ◽

Brain Signals ◽

Before And After ◽

Conventional Electrode ◽

The Brain

This study has been conducted to examine the effectiveness of the eye massaging device to reduce massive amount of eyesight problem. The electrical activity of the muscles surrounding the eyes is recorded by using Neurofax EEG-9200 machine. Electroencephalography (EEG) is a process to determine the brain signal, while Electrooculography (EOG) is used to measure the biopotential produced by the changes in eye position and eye movement occurred. The conventional electrode setting (also called 10-20) system is applied on the scalp electrodes for EEG to record the brain signals. While five electrodes on the forehead is used to record EOG signals. Channel O1 and O2 that act as visual processing is selected in order to record EEG signals. The signal is analyzed using Wavelet Transform and the useful parameter, Energy of Approximation (Ea) was extracted. In this study, t-test analysis is used to validate the differences of data produced before and after using eye massaging device. Based on the results, the average value collected for EEG signals before using the eye massaging device has been decreased for both channel with the different (O1: 5.083, O2: 3.385). Thus, it is proved that the eye massaging device exhibit difference for each movement tested.

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Measuring and modeling selective attention in early visual processing

PsycEXTRA Dataset ◽

10.1037/e536982012-174 ◽

1997 ◽

Cited By ~ 1

Author(s):

George Sperling ◽

Shui-I. Shih

Keyword(s):

Selective Attention ◽

Visual Processing ◽

Early Visual Processing

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NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.095 ◽

2019 ◽

pp. 139-145

Author(s):

Selma Büyükgöze

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Electrode Placement ◽

Eeg Signals ◽

Non Invasive ◽

Brain Signals ◽

Brain States ◽

The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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Effect of sleep on memory for binding different types of visual information

10.31234/osf.io/5zcw7 ◽

2020 ◽

Author(s):

John J Shaw ◽

Zhisen Urgolites ◽

Padraic Monaghan

Keyword(s):

Visual Information ◽

Storage Capacity ◽

Declarative Memory ◽

Recognition Task ◽

Forced Choice ◽

Long Term Memory ◽

Term Memory ◽

Different Types ◽

Types Of Information

Visual long-term memory has a large and detailed storage capacity for individual scenes, objects, and actions. However, memory for combinations of actions and scenes is poorer, suggesting difficulty in binding this information together. Sleep can enhance declarative memory of information, but whether sleep can also boost memory for binding information and whether the effect is general across different types of information is not yet known. Experiments 1 to 3 tested effects of sleep on binding actions and scenes, and Experiments 4 and 5 tested binding of objects and scenes. Participants viewed composites and were tested 12-hours later after a delay consisting of sleep (9pm-9am) or wake (9am-9pm), on an alternative forced choice recognition task. For action-scene composites, memory was relatively poor with no significant effect of sleep. For object-scene composites sleep did improve memory. Sleep can promote binding in memory, depending on the type of information to be combined.

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How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽

10.3390/electronics10060741 ◽

2021 ◽

Vol 10 (6) ◽

pp. 741

Author(s):

Yuseok Ban ◽

Kyungjae Lee

Keyword(s):

High Precision ◽

Recommender System ◽

Visual Information ◽

Experimental Results ◽

Supplementary Information ◽

Digital Platform ◽

Different Types ◽

Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

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