Papers by Włodzisław Duch
World Scientific Publishing eBooks, 2007
Neurocognitive approach to higher cognitive functions that bridges the gap between psychological ... more Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed.
Przegląd Psychologiczny, 2021
CelCelem badań była ocena wpływu deficytów poznawczych obecnych w specyficznym zaburzeniu w uczen... more CelCelem badań była ocena wpływu deficytów poznawczych obecnych w specyficznym zaburzeniu w uczeniu się matematyki, na operowanie mentalną osią liczbową przy użyciu jednocyfrowych liczb prezentowanych w formacie symbolicznym i niesymbolicznym. MetodaZbadano zdolność szacowania miejsca liczb na osi (ang. Number Line Estimation, NLE) u 20 dzieci z zaburzeniami w zakresie nauki matematyki (mathematical learning disabilities, MLD) i 27 ich typowo rozwijających się rówieśników (typically developing, TD). Wykorzystano w tym celu zadanie szacowania miejsca liczb na osi dla liczb z zakresu 1–9 przedstawianych w formacie symbolicznym i niesymbolicznym. WynikiW przypadku wszystkich dzieci większą wartość błędu szacowania uzyskano dla liczb ze środka osi liczbowej, aczkolwiek efekt był bardziej wyraźny w grupie z zaburzeniami. Co więcej, dzieci z obu grup w podobnym stopniu przeszacowywały, zaś różniły się pod względem niedoszacowywania miejsca liczb. Dzieci z grupy MLD ujawniły większe odchyl...
Acta Neurobiologiae Experimentalis, 2001
Bio-Algorithms and Med-Systems, 2020
ObjectivesDisorders of consciousness are very big medical and social problem. Their variability, ... more ObjectivesDisorders of consciousness are very big medical and social problem. Their variability, problems in precise definition and proper diagnosis make difficult assessing their causes and effectiveness of the therapy. In the paper we present our point of view to a problem of consciousness and its most common disorders.MethodsFor this moment scientists do not know exactly, if these disorders can be a result of simple but general mechanism, or a complex set of mechanisms, both on neural, molecular or system level. Presented in the paper simulations using neural network models, including biologically relevant consciousness’ modelling, help assess influence of specified causes.ResultsNonmotoric brain activity can play important role within diagnostic process as a supplementary method for motor capabilities. Simple brain sensory (e.g. visual) processing of both healthy subject and people with consciousness disorders help checking hypotheses in the area of consciousness’ disorders’ mec...
Bio-Algorithms and Med-Systems, 2018
The topic of brain stem computational simulation still seems understudied in contemporary scienti... more The topic of brain stem computational simulation still seems understudied in contemporary scientific literature. Current advances in neuroscience leave the brain stem as one of the least known parts of the human central nervous system. Brain stem lesions are particularly damaging to the most important physiological functions. Advances in brain stem modeling may influence important issues within the core of neurology, neurophysiology, neurosurgery, and neurorehabilitation. Direct results may include both development of knowledge and optimization and objectivization of clinical practice in the aforementioned medical areas. Despite these needs, progress in the area of computational brain stem models seems to be too slow. The aims of this paper are both to recognize the strongest limitations in the area of computational brain stem simulations and to assess the extent to which current opportunities may be exploited. Despite limitations, the emerging view of the brain stem provided by its...
IEEE Transactions on Biomedical Engineering
OBJECTIVE Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI)... more OBJECTIVE Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been frequently used to improve the accuracy of EEG-based BCIs for a new user (target domain), by making use of labeled data from a previous user (source domain). However, this raises privacy concerns, as EEG contains sensitive health and mental information. It is very important to perform privacy-preserving domain adaptation, which simultaneously improves the classification accuracy for a new user and protects the privacy of a previous user. METHODS We propose augmentation-based source-free adaptation (ASFA), which consists of two parts: 1) source model training, where a novel data augmentation approach is proposed for MI EEG signals to improve the cross-subject generalization performance of the source model; and, 2) target model training, which simultaneously considers uncertainty reduction for domain adaptation and consistency regularization for robustness. ASFA only needs access to the source model parameters, instead of the raw EEG data, thus protecting the privacy of the source domain. We further extend ASFA to a stricter privacy-preserving scenario, where the source model's parameters are also inaccessible. RESULTS Experimental results on four MI datasets demonstrated that ASFA outperformed 15 classical and state-of-the-art MI classification approaches. SIGNIFICANCE This is the first work on completely source-free domain adaptation for EEG-based BCIs. Our proposed ASFA achieves high classification accuracy and strong privacy protection simultaneously, important for the commercial applications of EEG-based BCIs.
WORLD SCIENTIFIC eBooks, May 1, 2007
Neurocognitive approach to higher cognitive functions that bridges the gap between psychological ... more Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed.
Lecture Notes in Computer Science, 2011
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Springer eBooks, 2006
Neural networks use their hidden layers to transform input data into linearly separable data clus... more Neural networks use their hidden layers to transform input data into linearly separable data clusters, with a linear or a perceptron type output layer making the final projection on the line perpendicular to the discriminating hyperplane. For complex data with multimodal distributions this transformation is difficult to learn. Projection on k ≥ 2 line segments is the simplest extension of linear separability, defining much easier goal for the learning process. The difficulty of learning non-linear data distributions is shifted to separation of line intervals, making the main part of the transformation much simpler. For classification of difficult Boolean problems, such as the parity problem, linear projection combined with k-separability is sufficient.
Studies in computational intelligence, 2007
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Springer eBooks, 2004
Quality of neural network mappings may be evaluated by visual inspection of hidden and output nod... more Quality of neural network mappings may be evaluated by visual inspection of hidden and output node activities for the training dataset. This paper discusses how to visualize such multidimensional data, introducing a new projection on a lattice of hypercube nodes. It also discusses what type of information one may expect from visualization of the activity of hidden and output layers. Detailed analysis of the activity of RBF hidden nodes using this type of visualization is presented in the companion paper.
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Papers by Włodzisław Duch