scholarly journals Decoding neural responses to motion-in-depth using EEG

2019 ◽  
Author(s):  
Marc M. Himmelberg ◽  
Federico G. Segala ◽  
Ryan T. Maloney ◽  
Julie M. Harris ◽  
Alex R. Wade

AbstractTwo stereoscopic cues that underlie the perception of motion-in-depth (MID) are changes in retinal disparity over time (CD) and interocular velocity differences (IOVD). These cues have independent spatiotemporal sensitivity profiles, depend upon different low-level stimulus properties, and are potentially processed along separate cortical pathways. Here, we ask whether these MID cues code for different motion directions: do they give rise to discriminable patterns of neural signals, and is there evidence for their convergence onto a single ‘motion-in-depth’ pathway? To answer this, we use a decoding algorithm to test whether, and when, patterns of electroencephalogram (EEG) signals measured from across the full scalp, generated in response to CD- and IOVD-isolating stimuli moving towards or away in depth can be distinguished. We find that both MID cue type and 3D-motion direction can be decoded at different points in the EEG timecourse and that direction decoding cannot be accounted for by static disparity information. Remarkably, we find evidence for late processing convergence: IOVD motion direction can be decoded relatively late in the timecourse based on a decoder trained on CD stimuli, and vice versa. We conclude that early CD and IOVD direction decoding performance is dependent upon fundamentally different low-level stimulus features, but that later stages of decoding performance may be driven by a central, shared pathway that is agnostic to these features. Overall, these data are the first to show that neural responses to CD and IOVD cues that move towards and away in depth can be decoded from EEG signals, and that different aspects of MID-cues contribute to decoding performance at different points along the EEG timecourse.

2020 ◽  
Vol 14 ◽  
Author(s):  
Marc M. Himmelberg ◽  
Federico G. Segala ◽  
Ryan T. Maloney ◽  
Julie M. Harris ◽  
Alex R. Wade

Two stereoscopic cues that underlie the perception of motion-in-depth (MID) are changes in retinal disparity over time (CD) and interocular velocity differences (IOVD). These cues have independent spatiotemporal sensitivity profiles, depend upon different low-level stimulus properties, and are potentially processed along separate cortical pathways. Here, we ask whether these MID cues code for different motion directions: do they give rise to discriminable patterns of neural signals, and is there evidence for their convergence onto a single “motion-in-depth” pathway? To answer this, we use a decoding algorithm to test whether, and when, patterns of electroencephalogram (EEG) signals measured from across the full scalp, generated in response to CD- and IOVD-isolating stimuli moving toward or away in depth can be distinguished. We find that both MID cue type and 3D-motion direction can be decoded at different points in the EEG timecourse and that direction decoding cannot be accounted for by static disparity information. Remarkably, we find evidence for late processing convergence: IOVD motion direction can be decoded relatively late in the timecourse based on a decoder trained on CD stimuli, and vice versa. We conclude that early CD and IOVD direction decoding performance is dependent upon fundamentally different low-level stimulus features, but that later stages of decoding performance may be driven by a central, shared pathway that is agnostic to these features. Overall, these data are the first to show that neural responses to CD and IOVD cues that move toward and away in depth can be decoded from EEG signals, and that different aspects of MID-cues contribute to decoding performance at different points along the EEG timecourse.


2020 ◽  
Author(s):  
Deon T. Benton ◽  
David H. Rakison

The ability to reason about causal events in the world is fundamental to cognition. Despite the importance of this ability, little is known about how adults represent causal events, what structure or form those representations take, and what the mechanism is that underpins such representations. We report four experiments with adults that examine the perceptual basis on which adults represent four-object launching sequences (Experiments 1 and 2), whether adults representations reflect sensitivity to the causal, perceptual, or causal and perceptual relation among the objects that comprise such sequences (Experiment 3), and whether such representations extend beyond spatiotemporal contiguity to include other low-level stimulus features such as an object’s shape and color (Experiment 4). Based on these results of the four experiments, we argue that a domain-general associative mechanism, rather a modular, domain-specific, mechanism subserves adults’ representations of four-object launching sequences.


2019 ◽  
Vol 10 (3) ◽  
Author(s):  
Jaeyoon Kim

Abstract For this study, I completed a comprehensive review of punishment clauses in the Korean Legal Code from 1985 to 2016. Using a web crawler and text analysis, I gathered data on the laws and then identified the content of the penal sentence in each clause. By investigating the data, I was able to quantify and assess changes over time in: (1) the number of punishment clauses; (2) the severity of sentences; and (3) the balance between imprisonment and fines. In order to examine the causes of these changes, I separated the data into different sentence levels and sectors. I found that low-level punishment clauses had grown quickly, and some of the sectors responsible for the change included civil engineering and sex offenses. This comprehensive review of the penal sentences revealed issues of concern related to overcriminalization, overpenalization, and an imbalance of punishment level in the Korean Legal Code.


2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.


Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.


Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.


2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.


2010 ◽  
Vol 22 (11) ◽  
pp. 2638-2651 ◽  
Author(s):  
Joel L. Voss ◽  
Heather D. Lucas ◽  
Ken A. Paller

Familiarity and recollection are qualitatively different explicit-memory phenomena evident during recognition testing. Investigations of the neurocognitive substrates of familiarity and recollection, however, have typically disregarded implicit-memory processes likely to be engaged during recognition tests. We reasoned that differential neural responses to old and new items in a recognition test may reflect either explicit or implicit memory. Putative neural correlates of familiarity in prior experiments, for example, may actually reflect contamination by implicit memory. In two experiments, we used obscure words that subjects could not formally define to tease apart electrophysiological correlates of familiarity and one form of implicit memory, conceptual priming. In Experiment 1, conceptual priming was observed for words only if they elicited meaningful associations. In Experiment 2, two distinct neural signals were observed in conjunction with familiarity-based recognition: late posterior potentials for words that both did and did not elicit meaningful associations and FN400 potentials only for the former. Given that symbolic meaning is a prerequisite for conceptual priming, the combined results specifically link late posterior potentials and FN400 potentials with familiarity and conceptual priming, respectively. These findings contradict previous interpretations of FN400 potentials as generic signals of familiarity and show that repeated stimuli in recognition tests can engender facilitated processing of conceptual information in addition to retrieval processing that leads to the awareness of memory retrieval. The different characteristics of the electrical markers of these two types of process further underscore the biological validity of the distinction between implicit memory and explicit memory.


2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.


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