scholarly journals Health Profiling Using Event-Related Potential (ERP) Brain Signals and Spiking Neural Network (SNN)

2020 ◽  
Vol 6 (2) ◽  
pp. 22-28
Author(s):  
Cut Amalia Saffiera ◽  
Raini Hassan ◽  
Amelia Ritahani Ismail
Keyword(s):  
Neural Network ◽  
Brain Activity ◽  
Mean Amplitude ◽  
Event Related Potential ◽  
Spiking Neural Network ◽  
Self Assessment ◽  
Brain Responses ◽  
Health Related ◽  
Response Biases ◽  
Occipital Lobes

Unhealthy lifestyles, especially on nutritional factors have become a major problem causing many diseases in Malaysians in recent years. Identification of lifestyle profiles such as preventive for individuals who adopt healthy and curative for individuals who do not maintain their lifestyle is needed to increase their awareness regarding their lifestyle. Because self-assessment is known to be vulnerable to produce response biases that lead to misclassification, identification of profiles based on brain responses needs to be done. An Event-related potential (ERP) is the main tools of cognitive neurologists and make ideal techniques for studying perception and attention. This research captured brain activity using electroencephalography (EEG) during receiving images of healthy and unhealthy foods that act as health-related stimuli. These EEG signals converted mathematically into the ERP signals and entered into the classification interface as input. In terms of classification, the methodology used is a dynamic developing Spiking Neural Network (deSSN) based on the Neucube architecture. ERP analysis results shown the mean amplitude of the LPP component in the Parietal and Occipital lobes is higher for healthy food in the preventive group. Whereas in the curative group it has been shown to be higher for unhealthy foods. This result is thought to reflect their preference in choosing food in their daily lifestyle. However, the results of the classification have shown that unhealthy food stimulation in the LPP wave showed superior results compared to data analysis in other conditions. Classification with ERP data is believed to support the results of self-assessment and build methods of making profiles that are more accurate and reliable.

scholarly journals P300 ERP Component on Eating Habits Profiling Using Dynamic Evolving Spiking Neural Network (deSNN)

2020 ◽  
Vol 6 (2) ◽  
pp. 29-35
Author(s):  
Cut Amalia Saffiera ◽  
Raini Hassan ◽  
Amelia Ritahani Ismail
Keyword(s):  
Neural Network ◽  
Eating Habits ◽  
Mean Amplitude ◽  
Event Related Potential ◽  
Spiking Neural Network ◽  
Self Assessment ◽  
Unhealthy Eating ◽  
The Status ◽  
Healthy Eating Habits ◽  
Evolving Spiking Neural Network

— Unhealthy eating habits have become a big issue that often causes many chronic diseases in various countries in recent years. The current assessment to identify the status of eating habits is to use self-assessment. However, self-assessment is known to have an error or uncertainty value due to cognitive factors from respondents that affect the results of the assessment. A person's profile is potentially measured by reviewing Event-related potential (ERP) which is an ideal technique for understanding perception and attention. This study uses images of healthy and unhealthy foods as a stimulus when recording EEG data. The method used for classification is dynamic evolving spiking neural network (deSSN) based on the Neucube architecture. The results showed that the mean amplitude of the P300 component discovered in the Parietal and Occipital lobes was higher for healthy food in the healthy eating habits group. Whereas the unhealthy eating habits group was higher for unhealthy foods. The deSNN classification is proven to operate in learning ERP data but the accuracy rate is not too high due to inadequate sample training

scholarly journals Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7354
Author(s):  
Zohreh Doborjeh ◽  
Maryam Doborjeh ◽  
Mark Crook-Rumsey ◽  
Tamasin Taylor ◽  
Grace Y. Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Temporal Dynamics ◽  
Psychological Intervention ◽  
Task Demands ◽  
Mindfulness Training ◽  
Event Related Potential ◽  
Spiking Neural Network ◽  
Auditory Oddball ◽  
Brain States

Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neural network to investigate the effect of mindfulness training on electroencephalographic function. Participants completed a 4-tone auditory oddball task (that included targets and physically similar distractors) at three assessment time points. In Group A (n = 10), these tasks were given immediately prior to 6-week mindfulness training, immediately after training and at a 3-week follow-up; in Group B (n = 10), these were during an intervention waitlist period (3 weeks prior to training), pre-mindfulness training and post-mindfulness training. Using a spiking neural network (SNN) model, we evaluated concurrent neural patterns generated across space and time from features of electroencephalographic data capturing the neural dynamics associated with the event-related potential (ERP). This technique capitalises on the temporal dynamics of the shifts in polarity throughout the ERP and spatially across electrodes. Findings support anteriorisation of connection weights in response to distractors relative to target stimuli. Right frontal connection weights to distractors were associated with trait mindfulness (positively) and depression (inversely). Moreover, mindfulness training was associated with an increase in connection weights to targets (bilateral frontal, left frontocentral, and temporal regions only) and distractors. SNN models were superior to other machine learning methods in the classification of brain states as a function of mindfulness training. Findings suggest SNN models can provide useful information that differentiates brain states based on distinct task demands and stimuli, as well as changes in brain states as a function of psychological intervention.

scholarly journals The role of trait empathy in the processing of observed actions in a false-belief task

2020 ◽  
Vol 15 (1) ◽  
pp. 53-61
Author(s):  
Christian Bellebaum ◽  
Marta Ghio ◽  
Marie Wollmer ◽  
Benjamin Weismüller ◽  
Patrizia Thoma
Keyword(s):  
Brain Activity ◽  
Event Related Potential ◽  
False Belief ◽  
Reactivity Index ◽  
False Belief Task ◽  
Linear Mixed Effects ◽  
Brain Responses ◽  
Trait Empathy ◽  
Empathy Quotient

Abstract Empathic brain responses are characterized by overlapping activations between active experience and observation of an emotion in another person, with the pattern for observation being modulated by trait empathy. Also for self-performed and observed errors, similar brain activity has been described, but findings concerning the role of empathy are mixed. We hypothesized that trait empathy modulates the processing of observed responses if expectations concerning the response are based on the beliefs of the observed person. In the present study, we utilized a false-belief task in which observed person’s and observer’s task-related knowledge were dissociated and errors and correct responses could be expected or unexpected. While theta power was generally modulated by the expectancy of the observed response, a negative mediofrontal event-related potential (ERP) component was more pronounced for unexpected observed actions only in participants with higher trait empathy (assessed by the Empathy Quotient), as revealed by linear mixed effects analyses. Cognitive and affective empathy, assessed by the Interpersonal Reactivity Index, were not significantly related to the ERP component. The results suggest that trait empathy can facilitate the generation of predictions and thereby modulate specific aspects of the processing of observed actions, while the contributions of specific empathy components remain unclear.

Behavior in the Brain

2010 ◽  
Vol 24 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Martin M. Monti ◽  
Adrian M. Owen
Keyword(s):  
Motor Behavior ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Motor Output ◽  
The Unconscious ◽  
Ethical Implications ◽  
Brain Responses ◽  
Minimally Conscious ◽  
Brain Insults ◽  
Brain Behavior

Recent evidence has suggested that functional neuroimaging may play a crucial role in assessing residual cognition and awareness in brain injury survivors. In particular, brain insults that compromise the patient’s ability to produce motor output may render standard clinical testing ineffective. Indeed, if patients were aware but unable to signal so via motor behavior, they would be impossible to distinguish, at the bedside, from vegetative patients. Considering the alarming rate with which minimally conscious patients are misdiagnosed as vegetative, and the severe medical, legal, and ethical implications of such decisions, novel tools are urgently required to complement current clinical-assessment protocols. Functional neuroimaging may be particularly suited to this aim by providing a window on brain function without requiring patients to produce any motor output. Specifically, the possibility of detecting signs of willful behavior by directly observing brain activity (i.e., “brain behavior”), rather than motoric output, allows this approach to reach beyond what is observable at the bedside with standard clinical assessments. In addition, several neuroimaging studies have already highlighted neuroimaging protocols that can distinguish automatic brain responses from willful brain activity, making it possible to employ willful brain activations as an index of awareness. Certainly, neuroimaging in patient populations faces some theoretical and experimental difficulties, but willful, task-dependent, brain activation may be the only way to discriminate the conscious, but immobile, patient from the unconscious one.

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters

scholarly journals Radar Emitter Identification Based on Fully Connected Spiking Neural Network

2021 ◽  
Vol 1914 (1) ◽  
pp. 012036
Author(s):  
LI Wei ◽  
Zhu Wei-gang ◽  
Pang Hong-feng ◽  
Zhao Hong-yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Fully Connected

scholarly journals Spatial Memory in a Spiking Neural Network with Robot Embodiment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Spatial Memory ◽  
Internal Representation ◽  
Learning Curves ◽  
Global Network ◽  
Spiking Neural Network ◽  
Interference Phenomenon ◽  
Brain Functioning ◽  
Information Characteristics ◽  
Catastrophic Interference

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

Sign in / Sign up

Export Citation Format

Share Document