A real time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities

A Poll Oriented Classifier for Affective Brain Computer Interfaces

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Classification of Emotional Signals from the DEAP dataset

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Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Olfactory perceptual degradation refers to the inability of people to recognize the variation in concentration levels of olfactory stimuli. The paper attempts to assess the degree of olfactory perceptual degradation of subjects from their hemodynamic response to olfactory stimuli. This is done in 2 phases. In the first (training) phase, a regression model is developed to assess the degree of concentration levels of an olfactory stimulus by a subject from her hemodynamic response to the stimulus. In the second (test) phase, the model is employed to predict the possible concentration level experienced by the subject in [0, 100] scale. The difference between the model-predicted response and the oral response (the center value of the qualitative grades) of the subject about her perceived concentration level is regarded as the quantitative measure of the degree of subject's olfactory degradation. The novelty of the present research lies in the design of a General Type-2 fuzzy regression model, which is capable of handling uncertainty due to the presence of intra- and inter-session variations in the brain responses to olfactory stimuli. The attractive feature of the paper lies in adaptive tuning of secondary membership functions to reduce model prediction error in an evolutionary optimization setting. The effect of such adaptation in secondary measures is utilized to adjust the corresponding primary memberships in order to reduce the uncertainty involved in the regression process. The proposed regression model has good prediction accuracy and high time-efficiency as evident from average percentage success rate (PSR) and run-time complexity analysis respectively. The Friedman test undertaken also confirms the superior performance of the proposed technique with other competitive techniques at 95% confidence level

Biological Intelligence: From Behavior to Learning Theory

Knowing how to learn, think, and act is not just a hallmark of intelligence, but a necessity of survival for many organisms. Behavior, the complete set of actions of species, allows us to glimpse into the minds of humans and animals, and by extension, intelligence itself. Biological intelligence is characterized by fast adaptation to changes and challenges, which is what allows species to survive in natural environments from starvation and predation. To study learning in a controlled setting, we can observe the behavior evoked through decision-making tasks that make it possible to quantify and analyze learning. By modeling the extracted behavioral features, we could start to understand the possible underlying mechanisms by proposing neural theory models, and look for those signals in the brain. Understanding the neural mechanisms of learning also strengthens the basis for building intelligent machines that are flexible and adaptive to the nonstationary world we live in. In this thesis, I present works in (1) automating behavioral setups and modeling suboptimal behavior in a traditional decision-making task, (2) using an ethological navigation task to characterize fast-sequence learning, and (3) how neural theory can explain some core behavioral phenomena in (2), and be used to solve a central problem in graph search.</p

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

Olfactory consciousness across disciplines

Our sense of smell pervasively influences our most common behaviors and daily experience, yet little is known about olfactory consciousness. Over the past decade and a half research in both the fields of Consciousness Studies and Olfaction has blossomed, however, olfactory consciousness has received little to no attention. The olfactory systems unique anatomy, functional organization, sensory processes, and perceptual experiences offers a fecund area for exploring all aspects of consciousness, as well as a external perspective for re-examining the assumptions of contemporary theories of consciousness. It has even been suggested that the olfactory system may represent the minimal neuroanatomy that is required for conscious processing. Given the variegated nature of research on consciousness, we include original papers concerning the nature of olfactory consciousness. The scope of the special edition widely incorporates olfaction as it relates to Consciousness, Awareness, Attention, Phenomenal- or Access-Consciousness, and Qualia. Research concerning olfaction and cross-modal integration as it relates to conscious experience is also address. As the initial foray into this uncharted area of research, we include contributions from across all disciplines contributing to cognitive neuroscience, including neurobiology, neurology, psychology, philosophy, linguistics, and computer sciences. It is our hope that this Research Topic will serve as the impetus for future interdisciplinary research on olfaction and consciousness

Relações entre características do autismo, variáveis emocionais e o processamento olfativo na população geral

Although altered sensory processing is recognized as a key-feature of Autism Spectrum Disorder (henceforth “autism”), olfactory functioning is still poorly understood in this condition. Considering the role of olfaction in human social communication and well-being, it is crucial to investigate which variables are related to the often-observed inconsistent results concerning olfactory functioning in autism. Study of the expression of autism traits and other autism-related variables in the general population may be useful to understand which specific dimensions are related to the often-observed symptoms, alterations, and heterogeneity in the autism spectrum, including in the olfactory domain. The present work sought to contribute to the multidimensional assessment of anxiety and autism traits in adults of the general population, as well as to the understanding of the multivariate relationships between autism characteristics, olfactory processing, anxiety, and alexithymia. Study 1 and Study 2 aimed to extend the available evidence about the psychometric properties of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) and the Autism Spectrum Quotient (AQ). Results supported the adequacy of both instruments to measure anxiety and autism traits, respectively, in a multidimensional perspective. Consistent with the literature, Study 1 found support for a four-factor, as well as a two-factor structure within the state and traits forms of the STICSA. Moreover, measurement invariance across sex groups, and good nomological validity were also supported for the STICSA. Results also suggested that the cognitive and somatic dimensions of trait anxiety, as measured by the STICSA, are differently related with the subjective and psychophysiological responses in distinct emotional contexts. Results of Study 2 further supported a three-factor structure of the AQ, consistent with previous studies, as well as the role of alexithymia, particularly difficulties in identifying feelings, as a mediator of the relationship between autism traits and trait anxiety. Study 3 analyzed the impact of the social skills and attention to detail dimensions of autism traits, and cognitive/somatic trait anxiety, on the olfactory abilities of the general population. Results emphasized the roles of sex, attention to detail and trait-somatic anxiety as significant predictors of odor discrimination abilities. Finally, Study 4 provided an integrative review about olfactory processing in autism and how advancing research in this area may benefit the knowledge and practice regarding social cognition and behavior in autism. The findings of this research highlight the need to explore the distinct dimensions of autism-related variables to better understand their complex relationships and impact in the functioning of the spectrum, including in olfactory functioning.Embora alterações no processamento sensorial sejam uma característica-chave da Perturbação do Espetro do Autismo (daqui em diante “autismo”), o funcionamento olfativo ainda é pouco compreendido nesta condição. Considerando o papel do olfato na comunicação, interação social e bem-estar, é crucial investigar que variáveis estão relacionadas com os resultados inconsistentes frequentemente observados no âmbito do processamento olfativo no autismo. Estudar a expressão de traços de autismo na população geral, bem como a expressão multidimensional de outras variáveis relacionadas, pode ser útil para compreender que dimensões estão relacionadas com os sintomas, alterações e heterogeneidade frequentemente observados no autismo, incluindo no domínio olfativo. O presente trabalho pretendeu contribuir para a avaliação multidimensional da ansiedade e de traços de autismo em adultos da população geral, bem como para uma melhor compreensão da relação multivariada entre as características do autismo, processamento olfativo, ansiedade e alexitimia. O Estudo 1 e o Estudo 2 tiveram como objetivo estender a evidência disponível sobre as propriedades psicométricas do State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA) e do Autism Spectrum Quotient (AQ). Os resultados suportaram a adequação de ambos os instrumentos para medir ansiedade e traços de autismo, respetivamente, numa perspetiva multidimensional. Em linha com a literatura, o Estudo 1 providenciou suporte para uma estrutura de quatro fatores, bem como para uma estrutura de dois fatores dentro das dimensões de ansiedade traço e estado do STICSA. Observou-se ainda invariância fatorial considerando a variável sexo, assim como boa validade nomológica. Os resultados também sugeriram que as dimensões cognitivas e somáticas da ansiedade traço, medidas pelo STICSA, estão relacionadas de forma distinta com as respostas subjetiva e psicofisiológica em diferentes contextos emocionais. Os resultados do Estudo 2, de modo consistente com estudos anteriores, suportaram uma estrutura de três fatores do AQ, bem como o papel da alexitimia, particularmente das dificuldades em identificar sentimentos e emoções, como mediadora da relação entre traços de autismo e ansiedade traço. O Estudo 3 analisou o impacto das dimensões de traços de autismo relacionadas com as capacidades sociais e atenção para os detalhes, e da ansiedade traço cognitiva/somática, nas capacidades olfativas da população geral. Os resultados evidenciaram o papel das variáveis sexo, atenção para os detalhes e ansiedade traço somática como preditores significativos da capacidade de discriminação olfativa. Por fim, o Estudo 4 apresentou uma revisão integrativa sobre o processamento olfativo no autismo, e como o avanço da investigação nesta área pode beneficiar o conhecimento e a prática no âmbito da cognição e comportamento social. Os resultados desta investigação destacam a importância de explorar as diferentes dimensões das variáveis relacionadas com o autismo para melhor compreender a complexidade das suas relações e impacto no funcionamento do espetro, incluindo no que diz respeito ao funcionamento olfativo.Programa Doutoral em Psicologi