Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals

Emotions play a significant role in the cognitive processes of the human brain, such as decision making, learning and perception. The use of physiological signals has shown to lead to more objective, reliable and accurate emotion recognition combined with raising machine learning methods. Supervised learning methods have dominated the attention of the research community, but the challenge in collecting needed labels makes emotion recognition difficult in large-scale semi- or uncontrolled experiments. Unsupervised methods are increasingly being explored, however sub-optimal signal feature selection and label identification challenges unsupervised methods' accuracy and applicability. This article proposes an unsupervised deep cluster framework for emotion recognition from physiological and psychological data. Tests on the open benchmark data set WESAD show that deep k-means and deep c-means distinguish the four quadrants of Russell's circumplex model of affect with an overall accuracy of 87%. Seeding the clusters with the subject's subjective assessments helps to circumvent the need for labels.Comment: 7 pages, 1 figure, 2 table

Class discovery from semi-structured EEG data for affective computing and personalisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not explore the inter-relationships between the data collected missing out on any correlations that could tell us interesting facts beyond emotional recognition. This second issue would be of particular interest to psychologists and medical professions. In this paper, we investigate the use of Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be translated into classes. We start by training varying sizes of SOM with the EEG data provided in a public dataset (DEAP). The produced graphs showing Neighbour Distance, Sample Hits, Weight Position are analysed holistically to identify patterns in the structure. Following that, we have considered the ground- truth label provided in DEAP, in order to identify correlations between the label and the clustering produced by the SOM. The results show the potential of SOM for class discovery in this particular context. We conclude with a discussion on the implications of this work and the difficulties in evaluating the outcome

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Exploring behaviour patterns with self-organizing map for personalised mental stress detection

Abstract. Stress is an important health problem and the cause for many illnesses and working days lost. It is often measured with different questionnaires that capture only the current stress levels and may come in too late for early prevention. They are also prone to subjective inaccuracies since the feeling of stress, and the physiological response to it, have been found to be individual. Real-time stress detectors, trained on biosignals like heart rate variability, exist but majority of them employ supervised learning which requires collecting a large amount of labelled data from each system user. Commonly, they are tested in situations where the stress response is deliberately induced (e.g. laboratory). Thus they may not generalise to real-life conditions where more general behavioural data could be used. In this study the issues with labelling and individuality are addressed by fitting unsupervised stress detection models at several personalisation levels. The method explored, the Self-Organizing Map, is combined with different clustering algorithms to find personal, semi-personal and general behaviour patterns that are converted to stress predictions. Laboratory biosignal-data are used for method validation. To provide an always-on type stress detection, real-life behavioural data consisting of biosignals and smartphone data are experimented on. The results show that personalisation does improve the predictions. The best classification performance for the laboratory data was found with the fully personalised model (F1-score 0.89 vs. 0.45 with the general model) but for the real-life data there was no big difference between fully personal (F1-score 0.57) and general model as long as the behaviour patterns were mapped to stress individually (F1-score 0.60). While the scores also validate the feasibility of SOM for mental stress detection, further research is needed to determine the most suitable and practical level of personalisation and an unambiguous mapping between behaviour patterns and stress.Tiivistelmä. Stressi on merkittävä terveysongelma ja syynä useisiin sairauksiin sekä työpoissaoloihin. Sitä mitataan usein erilaisilla kyselyillä, jotka kuvaavat vain hetkellistä stressitasoa ja joihin voidaan vastata liian myöhään ennaltaehkäisyn kannalta. Kyselyt ovat myös alttiita subjektiivisille epätarkkuuksille, koska stressintunteen, ja stressinaikaisten fysiologisten reaktioiden, on havaittu olevan yksilöllisiä. Reaaliaikaisia, biosignaalien kuten sykevälivaihtelun analyysiin perustuvia, stressintunnistimia on olemassa, mutta pääosin ne käyttävät ohjatun oppimisen menetelmiä, mikä vaatii jokaiselta järjestelmän käyttäjältä suuren stressintunteella merkityn aineiston. Stressintunnistimia myös usein testataan tilanteissa, joissa stressi on tahallisesti aiheutettua (esimerkiksi laboratoriossa). Siten ne eivät yleisty tosielämän tarpeisiin, jolloin voidaan käyttää yleisempää käyttäytymistä kuvaavaa aineistoa. Tässä tutkimuksessa vastataan datan merkintäongelmaan sekä yksilöllisyyden huomioimiseen käyttäen ohjaamattoman oppimisen stressintunnistusmalleja eri yksilöimisen tasoilla. Käytetty menetelmä, itseorganisoituva kartta, yhdistetään eri ryhmittelyalgoritmeihin tavoitteena löytää henkilökohtaiset, osin henkilökohtaiset sekä yleiset käyttäytymismallit, jotka muunnetaan stressiennusteiksi. Menetelmän sopivuuden vahvistamiseksi käytetään laboratoriossa kerättyä biosignaalidataa. Menetelmää sovelletaan myös tosielämän stressintunnistukseen biosignaaleista ja älypuhelimen käyttödatasta koostuvalla käyttäytymisaineistolla. Tulokset osoittavat, että yksilöiminen parantaa ennustetarkkuutta. Laboratorio-aineistolla paras luokittelutarkkuus löydettiin täysin yksilöllisellä mallilla (F1-pistemäärä 0.89, kun yleisellä 0.45). Tosielämän aineistolla täysin yksilöllisen (F1-pistemäärä 0.57) ja yleisen mallin, jossa käyttäytymismallien ja stressin välinen kuvaus määrättiin yksilöidysti (F1-pistemäärä 0.60), välinen ero ei ollut suuri. Vaikka tulokset vahvistavatkin itseorganisoituvan kartan sopivuuden psyykkisen stressin tunnistamisessa, lisätutkimusta tarvitaan määräämään soveltuvin ja käytännöllisin yksilöimisen taso sekä yksikäsitteinen kuvaus käyttäytymismallien ja stressin välille

A Probabilistic Exploration of Food Supplementation and Assistance

Food insecurity is a stark threat that grips our country and affects households throughout our country. Dietary insufficiency manifests itself in ways that affect health and public safety. According to researchers, individuals who suffer from food insecurity have a higher risk of aggression, anxiety, suicide ideation and depression. These problems tend to occur unequally distributed among those households with lower income. In this work, an exploratory analysis within these data sets will be performed to examine the socio-economic, biographical, nutritional, and geographical principal components of food insecurity among survey participants and how the US Supplemental Nutrition Assistance Program (SNAP) effects partakers of this study. Relevant statistical and algorithmic tools will be used such as Self organizing maps (SOMs) and hierarchical clustering will be used for cluster analysis in addition to logistic regression and random forests for propensity score matching. Final results show a positive effect on household wellbeing and increased food spending on SNAP participants

Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data

Physiological measurements involves observing variables that attribute to the normative functioning of human systems and subsystems directly or indirectly. The measurements can be used to detect affective states of a person with aims such as improving human-computer interactions. There are several methods of collecting physiological data, but wearable sensors are a common, non-invasive tool for accurate readings. However, valuable information is hard to extract from the raw physiological data, especially for affective state detection. Machine Learning techniques are used to detect the affective state of a person through labeled physiological data. A clear problem with using labeled data is creating accurate labels. An expert is needed to analyze a form of recording of participants and mark sections with different states such as stress and calm. While expensive, this method delivers a complete dataset with labeled data that can be used in any number of supervised algorithms. An interesting question arises from the expensive labeling: how can we reduce the cost while maintaining high accuracy? Semi-Supervised learning (SSL) is a potential solution to this problem. These algorithms allow for machine learning models to be trained with only a small subset of labeled data (unlike unsupervised which use no labels). They provide a way of avoiding expensive labeling. This paper compares a fully supervised algorithm to a SSL on the public WESAD (Wearable Stress and Affect Detection) Dataset for stress detection. This paper shows that Semi-Supervised algorithms are a viable method for inexpensive affective state detection systems with accurate results.Comment: 12 page

Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data

Wrist-worn wearable devices equipped with heart activity sensors can provide valuable data that can be used for preventative health. However, hearth activity analysis from these devices suffers from noise introduced by motion artifacts. Methods traditionally used to remove outliers based on motion data can yield to discarding clean data, if some movement was present, and accepting noisy data, i.e., subject was still but the sensor was misplaced. This work shows that self-organizing maps (SOMs) can be used to effectively accept or reject sections of heart data collected from unreliable devices, such as wrist-worn devices. In particular, the proposed SOM-based filter can accept a larger amount of measurements (less false negatives) with an higher overall quality with respect to methods solely based on statistical analysis of motion data. We provide an empirical analysis on real-world wearable data, comprising heart and motion data of users. We show how topographic mapping can help identifying and interpreting patterns in the sensor data and help relating them to an assessment of user state. More importantly, our experimental results show the proposed approach is able to retain almost twice the amount of data while keeping samples with an error that is an order of magnitude lower with respect to a filter based on accelerometric data