Narrow Results

We summarize our recent works using the simultaneous electroencephalography (EEG) and functional MRI (fMRI) to explore the high spatiotemporal human brain functions. This new non-invasive technique definitely enhances us to know more about the underlying neuronal activity.

This study is to examine the correlation between customers’ interactive experience with flower stores and their purchase intentions through the aesthetic experience along with the examination of customers’ brainwaves. Among a total of 56 effective questionnaires, 10 random participants are chosen to give electroencephalogram (EEG) examinations and interviews. Regression analyses showed the explained variance 57.9% and the regression coefficients 0.415 (interactive experience) and 0.422 (esthetic experience). The EEG in interactive experiences varied case by case, but delta tended to increase in aesthetic experiences.

The approach and results of extraction EEG wavelet spectra features and classification in this features space the early stages of posttraumatic epileptiform activity in experimental rats after brain injury, and Parkinson’s disease are described. Feature extraction method is based on wavelet spectrograms ridges, and local extrema points time– frequency distribution. Proposed methods and algorithms are used for post-traumatic epileptiform activity recognition in long durable EEG rat records before and after traumatic brain injury. Feature extraction and classification model of the early-stage Parkinson’s disease in EEG feature space can be applicable to disease risk group identification and screening.

Utilizing electroencephalography (EEG) for emotion recognition enables the direct collection of physiological signals, thereby circumventing potential deception associated with facial expressions and language cues. Concurrently, there has been a widespread adoption of deep learning techniques in this domain, aimed at achieving enhanced results through model and parameter adjustments. An innovative approach is introduced by incorporating the Fast Fourier Transform (FFT) for data preprocessing in conjunction with the CBi-GRU model, merging the Convolutional Neural Network (CNN) and the Bidirectional Gated Recurrent Unit (BiGRU). Comparative analysis underscores the significance of exploring the intrinsic characteristics of the data itself: the application of FFT not only substantially reduces processing time but also enhances model performance. The integration of CBiGRU and FFT leverages the strengths of both CNN and BiGRU methods, resulting in increased accuracy and rapid convergence.

The human brain has several major levels of functioning, e.g.: quantum, genetic, single neuron, neural networks, cognitive, each of them involving a complex dynamic interaction between participating elements and signals. There are also complex dynamic interactions across functional levels [1].

With the advancement of the bioinformatics and brain research technologies more data become available tracing the activity of genes, neurons, neural networks and brain areas over time [2,3,4]. How can such data be used to create a dynamic model that captures dynamic interactions at a particular level over time? How can we integrate dynamic models at different levels? How do we use these models to better understand brain dynamics? These are the main questions addressed in the paper through dynamic interaction network modelling.

Dynamic Interaction Networks (DIN) are popular methods [1] especially for modelling a biological gene regulatory network (GRN) of n genes expressed over time. A node Nj(t) in the model represents the gene Gj(t) activation (expression) at a particular time t and the weighted arcs Wij represent the degree of interaction between genes Gi (i=1,2,…,n) and Gj at two consecutive time moments t and (t+1). In order to evaluate Nj(t+1) a function Fj (Ni, Wij; i=1,2,…,n) is used. The regulatory network model is created through optimisation procedure that optimises the connection weight matrix W, the functions Fj (j=1,2,…,n) from time course gene expression data. There are several methods used so far to create a GRN from time course data.

The paper demonstrates how DIN can be used to model brain dynamics at different levels. At a genetic level, a GRN can be created for a single neuron involving a set of relevant to a particular neuronal function genes. At a next, more complex level, a GRN can be created to capture the dynamics of the expression of several genes related to a complex brain function of a brain area, such as LTP in the CA3 area of the hippocampus. A next level of complexity is to create a model that includes a GRN and matches not only gene expression values over time, but brain signals as well, such as EEG, related to a particular brain function or a disease, e.g. epilepsy. All these models are called computational neurogenetic models [5].

A DIN can also be created from a time series of EEG measurements related to perceptual or cognitive functions, such as visual and auditory perception. The DIN model represents EEG channels as nodes and the connection weight matrix represents the temporal interaction between the brain signals measured by the channels. It is demonstrated that building a DIN model from EEG data can help tracing and discovering hidden brain signal interactions for particular brain functions. Comparing DIN models, built on EEG data measured for different stimuli and different individuals, can also help understand differences in the brain functioning for different perception and cognitive tasks and for different individuals.

The paper demonstrates all the above levels of DIN modelling and illustrates them on real brain data. The paper suggests directions for further research in modelling and discovery of dynamic brain interactions relating to complex brain functions at different functional level.

There is strong evidence pointing to the existence of sudden jumps and apparent discontinuities in spatio-temporal correlates of cognitive activity over the theta frequency band. This topic, however, is highly controversial as it requires revision of our traditional view on brain functions and the nature of intelligence. Human cognition performs a granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehendible chunks of fuzzy concepts and complex behavioral schemas. They are accessed during future action selection and decisions. In this work a dynamical approach to higher cognition and intelligence is presented to interpret experimental findings. In the model, meaningful knowledge is continuously created, processed, and dissipated in the form of sequences of oscillatory patterns of neural activity distributed across space and time. Oscillatory patterns can be viewed as intermittent manifestations of a generalized symbol system, with which brains compute. These patterns are not rigid but flexible and they dissipate soon after they have been generated through spatio-temporal phase transitions. The proposed biologically-motivated computing using dynamic patterns provides an alternative to the notoriously difficult symbol grounding problem and it has been implemented in computational and robotic environments.

We propose a simple and effective method of characterizing complexity of EEG-signals for monitoring the depth of anaesthesia using Higuchi's fractal dimension method. We demonstrate that the proposed method may compete with the widely used BIS monitoring method.

The purpose of this study is to verify that EEG is composed of frequency components with chaotic characteristics. EEG data are decomposed into frequency components, and then the chaos and fractal analyses are applied. More precisely, the running spectrum of EEG is derived and the chaos analysis is applied evaluating the Lyapunov spectrum and the correlation dimension. As a result, the existence of the chaotic phenomenon is found for most of the frequency components used as the objects of the analysis. It is also found that the fractal properties exist in most of the frequency components. The multifractal analysis is applied to the EEG data, and the q-th order generalised dimension is evaluated. It is seen as a result that the reconstructed attractors of EEG are characterized by a nonuniform fractal distributions, which are represented by a large number of scaling indices concerning with the dimension.

The Electroencephologram (EEG) experiments were designed in this paper to emulate our daily visual working memory task and demonstrate the effect of image target numbers and background textures on our brains’ visual working memory. This paper discovered that there is a possibly of implementing a neural network to predict memoryrelated brain activity in a visual working memory experiment, where participants were presented with images of different target item numbers and asked to remember as many target objects as possible. Both the Multi-Layer Perceptron (MLP) network and support vector machine (SVM) were used as training methods for the prediction. The prediction results are consistent with the actual EEG power variation observed in the experiment, which demonstrate the effect of target item number and background texture on the level of difficulty in image memorization.

In this chapter, a hybrid classification approach is proposed using the gray wolf optimizer (GWO) integrated with support vector machines (SVMs) to automatically detect the seizure in EEG. The discrete wavelet transform (DWT) was utilized to decompose EEG into five sub-band components. The SVM classifier was trained using various parameters that were extracted and used as feature. GWO was used to select a sub-feature. As the EEG signal rating depends on the optimal parameters that have been selected, it is also integrated with SVM to obtain better resolution of the classification by selecting the best tuning parameters SVM. The experimental results showed that the proposed GWO-SVM approach, capable of detecting epilepsy, could therefore further enhance the diagnosis of epilepsy.

A human brain is the most important organ which controls the functioning of the body including heartbeat and respiration. It is an extremely complex system. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. In clinical context EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time, say 20-40 minutes, as recorded from multiple electrodes placed on the scalp. The main application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. A secondary clinical use of EEG is in coma, Alzheimer's disease, encephalopathies, and brain death. However in the recent years EEG is also being used to design the brain of a robot. Mathematical concepts specially methods for numerical solution of partial differential equations with boundary conditions, inverse problem methods and wavelet analysis have found prominent position in the study of EEG. The present paper is devoted to this theme and will highlight the role of wavelet methods. It will also include the results obtained in our research project.

The aim of the study is to develop quantitative parameters of human electroencephalographic (EEG) recordings with epileptic seizures. We used long-lasting recordings from subjects with epilepsy obtained as part of their clinical investigation. The continuous wavelet transform of the EEG segments and the wavelet-transform modulus maxima method enable us to evaluate the energy spectra of the segments, to fin lines of local maximums, to gain the scaling exponents and to construct the singularity spectra. We have shown that the significant increase of the global energy with respect to background and the redistribution of the energy over the frequency range are observed in the patterns involving the epileptic activity. The singularity spectra expand so that the degree of inhomogenety and multifractality of the patterns enhances. Comparing the results gained for the patterns during different functional probes such as open and closed eyes or hyperventilation we demonstrate the high sensitivity of the analyzed parameters (the maximal global energy, the width and asymmetry of the singularity spectrum) for detecting the epileptic patterns.

Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.

In previous work¹ it has been shown that feature extraction algorithms based on Cellular Nonlinear Networks with nonlinear weight functions as well as discrete time Cellular Nonlinear Networks may contribute to the detection of predictive characteristics of an impending seizure. In this contribution results of two signal prediction approaches are presented – a retrospective study based on data of 15 patients with at least 4 seizures each, and first results of an approach to extract features by analyzing comparably short data segments only.

The evaluation, standardization, and reproducibility of studies in the field of seizure prediction has always been hampered by the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the European Epilepsy database EPILEPSIAE with long-term EEG recordings of 275 patients, annotated by EEG experts and supplemented with extensive meta-data. Since the first 60 datasets have been made available in 2012, we illustrate the content and structure of the database that will affect current standards in the field of prediction and facilitate reproducibility and comparison of studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and related topics including computational neuroscience.

Autism has been viewed as a highly heritable neurobiological condition of mysterious but presumably genetic origin, which involves lifelong neurocognitive, perceptual and emotional deficits. This conceptual framing has led to a focus on searching for underlying genetic causes of differences in the autistic brain, particularly in anatomical structure, that are presumed to be hardwired into the system.

More recently, it is becoming clear that genes alone do not create autism. The more inclusive emerging view is that genes and environment interact to influence epigenetics, cell signaling and physiology. This formulation goes beyond the idea that a preconceptional or prenatal gene-environment interaction definitively causes a person's autism, and instead emphasizes that the interplay of all of these levels develops over time contingent upon exposures, experiences and lifestyle choices, and that the behaviors are actively produced by living cells that function differently, rather than simply being caused by static brain wiring that is different from the start.

From this vantage point it is important to look freshly at how we think about the brain, and what it is that the brain does to create autistic behaviors. A multi-scaled, whole-body, dynamical approach to the processes of signal generation in the brain and how these processes are shaped by ongoing dynamic interplays of multiple modulators offers previously unappreciated opportunities for improvement of brain function.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive method of altering patterns of brain activity. rTMS has been applied to a wide variety of psychiatric and neurological conditions and is generally regarded as safe and has no lasting side effects. Within the context of autism spectrum disorders (ASD), rTMS has a unique application as a treatment modality. Recent evidence suggests the symptoms of ASD may be related to increased cortical excitability and, specifically, an increased ratio of cortical excitation to inhibition accompanied by deficiencies in long-range cortical connectivity. Locally over-connected neural networks may explain the superior ability of autistic children in isolated tasks (e.g., visual discrimination) while, at the same time, deficiencies in long-range connectivity may explain other features of the disorder (e.g., lack of social reciprocity). An increased ratio of cortical excitation to inhibition and higher-than-normal cortical “noise” may also explain the strong aversive reactions to auditory, tactile, and visual stimuli frequently recorded in autistic individuals as well as a higher incidence of epilepsy. Using specific parameters of stimulation, rTMS has been shown to increase cortical inhibition by selectively activating cortical inhibitory neurons. In a number of investigations, our group evaluated the effects of rTMS on indices of selective attention and executive functioning, as well as measures of social awareness, hyperactivity, irritability, and repetitive/stereotyped behavior. Subjects with ASD were assessed at baseline and following rTMS with electroencephalographic (EEG) and event-related potential (ERP) measures of selective attention and executive functioning. Subjects were also assessed for ASD symptomatology using neuropsychological questionnaires. Following rTMS, subjects showed significant improvement in EEG and ERP measures of selective attention and executive functioning, and also showed significant improvement in measures of irritability and repetitive/stereotyped behavior. Our preliminary findings in three experimental studies using 6-, 12-, and 18 session-long rTMS courses in children with autism indicate that rTMS has the potential to become an important therapeutic tool in research and treatment, and may play an important role in improving the quality of life for many individuals with ASD. Further research directions are proposed.

The nature of consciousness, the mechanism by which it occurs in the brain, and its ultimate place in the universe are unknown. We proposed in the mid 1990's that consciousness depends on biologically “orchestrated” coherent quantum processes in collections of microtubules within brain neurons, that these quantum processes correlate with, and regulate, neuronal synaptic and membrane activity, and that the continuous Schrödinger evolution of each such process terminates in accordance with the specific Diósi-Penrose (DP) scheme of “objective reduction” (“OR”) of the quantum state. This orchestrated OR activity (“Orch OR”) is taken to result in moments of conscious awareness and/or choice. The DP form of OR is related to the fundamentals of quantum mechanics and space-time geometry, so Orch OR suggests that there is a connection between the brain's biomolecular processes and the basic structure of the universe. Here we review Orch OR in light of criticisms and developments in quantum biology, neuroscience, physics and cosmology. We also introduce novel suggestions of (1) beat frequencies of faster Orch OR microtubule dynamics (e.g. megahertz) as a possible source of the observed electroencephalographic ("EEG") correlates of consciousness and (2) that OR played a key role in life's evolution. We conclude that consciousness plays an intrinsic role in the universe.

Finding human cognitive state is very important and useful for medical cares in our daily life. Eye state classification is a kind of common time-series problem for detecting human cognitive state. One of approaches of EEG eye state classification is based on time series data. We propose a new neural fuzzy structure that is possible to use an eye state classification. The classification accuracy can be achieved by using the proposed approach in terms of the number of neurons in the hidden layer, which also leads types of membership function in fuzzy rules. The data of EEG signals for monitoring eye state saved by Machine Learning Repository, University of California, Irvine (UCI) is used for eye state benchmarking. We did experiments with 3 different networks for the architecture by changing the number of neurons in the hidden layer and random seed for weights. We found that tuning parameters asymmetrically gave us the best results through test cases. According to the test results, we have the best result with 4 neurons by managing parameters of standard deviations asymmetrically, with which showed a 4.0% average error rate with the test data.