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 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 OVERVIEW OF HIGH SODIUM EATING HABITS BEFORE AND AFTER COVID-19 PANDEMIC IN INDONESIA

2021 ◽  
Vol 44 (2) ◽  
pp. 133-144
Author(s):  
Afina Rachma Sulistyaning ◽  
Farida Farida
Keyword(s):  
College Students ◽  
Eating Habits ◽  
Sodium Intake ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
High Sodium ◽  
System A ◽  
Unhealthy Eating ◽  
Before And After ◽  
Healthy Eating Habits

National and global reports showed a high prevalence of sodium intake above the recommended threshold. The pandemic situation might have altered people's eating habits into a healthier diet to improve the immunity system. A high-sodium diet, which has previously been reported as a substantial contributor to several degenerative diseases, might be considered unhealthy eating habits. This study aimed to analyze whether the Covid-19 pandemic has changed the eating habits of high sodium foods and drinks in college students. This cross-sectional study used a food frequency and perception questionnaire in December 2019 - August 2020, conducted in direct interviews and online questionnaires. Forty-three college students enrolled in the present study as respondents. The number of respondents with above-average high sodium eating habits decreased during the covid-19 pandemic, although not statistically significant (p 0.05). More than 60 percent of respondents admitted no significant changes in packaged foods and drinks intake, even though 79.1 percent of respondents reported healthier food and drinks intake during the Covid-19 pandemic. College students/adolescent needs to restrict their consumption of high sodium foods and drinks, especially during the Covid-19 pandemic to improve the immune system. It is also important to emphasize on the massive and continuous promotion of healthy eating habits among college students. Keywords: Covid-19, eating habits, sodium, pandemic ABSTRAK Data nasional dan global menunjukkan tingginya prevalensi konsumsi sodium diatas batas rekomendasi asupan. Kondisi pandemi Covid-19 dapat mengubah pola konsumsi masyarakat menjadi lebih sehat untuk meningkatkan sistem imun. Diet tinggi natrium dilaporkan sebagai penyebab penting dalam perkembangan berbagai penyakit degeneratif, sehingga dapat dikategorikan sebagai kebiasaan makan yang tidak sehat. Penelitian ini bertujuan untuk menganalisis apakah pandemi Covid-19 telah mengubah kebiasaan makan dan minum tinggi natrium di kalangan mahasiswa. Penelitian ini menggunakan metode cross-sectional dengan kuesioner FFQ dan persepsi makan. Penelitian ini berlangsung pada Desember 2019 – Agustus 2020 yang dilaksanakan secara wawancara langsung dan menggunakan kuesioner online. Responden terdiri dari 43 mahasiswa. Jumlah responden dengan pola konsumsi tinggi natrium menurun selama pandemi Covid-19 meskipun tidak signifikan (p 0.05). Lebih dari 60 persen responden mengakui tidak ada perubahan signifikan terkait konsumsi makanan dan minuman kemasan , meskipun 79.1 persen melaporkan konsumsi makanan dan minuman menjadi lebih sehat selama pandemi. Mahasiswa/remaja perlu mengurangi konsumsi makanan dan minuman tinggi natrium, terutama selama masa pandemi Covid-19 untuk meningkatkan sistem imun. Penting untuk diperhatikan bahwa promosi pola konsumsi makanan sehat di lingkup mahasiswa perlu dilakukan dengan langkah yang masif dan berkelanjutan. Kata kunci: Covid-19, pola makan, natrium, pandemi

A Biologically Inspired Evolving Spiking Neural Model with Rank-Order Population Coding and a Taste Recognition System Case Study

2011 ◽  
pp. 136-155 ◽  
Author(s):  
S. Soltic ◽  
N. Kasabov
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Rank Order ◽  
Neural Model ◽  
Recognition System ◽  
Population Coding ◽  
Spiking Neural Network ◽  
Biologically Inspired ◽  
Information Encoding ◽  
Evolving Spiking Neural Network

The human brain has an amazing ability to recognize hundreds of thousands of different tastes. The question is: can we build artificial systems that can achieve this level of complexity? Such systems would be useful in biosecurity, the chemical and food industry, security, in home automation, etc. The purpose of this chapter is to explore how spiking neurons could be employed for building biologically plausible and efficient taste recognition systems. It presents an approach based on a novel spiking neural network model, the evolving spiking neural network with population coding (ESNN-PC), which is characterized by: (i) adaptive learning, (ii) knowledge discovery and (iii) accurate classification. ESNN-PC is used on a benchmark taste problem where the effectiveness of the information encoding, the quality of extracted rules and the model’s adaptive properties are explored. Finally, applications of ESNN-PC in recognition of the increasing interest in robotics and pervasive computing are suggested.

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