Abstract: The error monitoring and processing system, EMPS is
the system located in the substantia nigra of the midbrain, basal
ganglia and cortex of the forebrain, and plays a leading role in error
detection and correction. The main components of EMPS are the
dopaminergic system and anterior cingulate cortex. Although, recent
studies show that alcohol disrupts the EMPS, the ways in which
alcohol affects this system are poorly understood. Based on current
literature data, here we suggest a hypothesis of alcohol-related
glucose-dependent system of error monitoring and processing, which
holds that the disruption of the EMPS is related to the competency of
glucose homeostasis regulation, which in turn may determine the
dopamine level as a major component of EMPS. Alcohol may
indirectly disrupt the EMPS by affecting dopamine level through
disorders in blood glucose homeostasis regulation.
Abstract: Deep Brain Stimulation or DBS is a surgical treatment for Parkinson-s Disease with three stimulation parameters: frequency, pulse width, and voltage. The parameters should be selected appropriately to achieve effective treatment. This selection now, performs clinically. The aim of this research is to study chaotic behavior of recorded tremor of patients under DBS in order to present a computational method to recognize stimulation optimum voltage. We obtained some chaotic features of tremor signal, and discovered embedding space of it has an attractor, and its largest Lyapunov exponent is positive, which show tremor signal has chaotic behavior, also we found out, in optimal voltage, entropy and embedding space variance of tremor signal have minimum values in comparison with other voltages. These differences can help neurologists recognize optimal voltage numerically, which leads to reduce patients' role and discomfort in optimizing stimulation parameters and to do treatment with high accuracy.
Abstract: In neural networks, when new patterns are learned by a network, the new information radically interferes with previously stored patterns. This drawback is called catastrophic forgetting or catastrophic interference. In this paper, we propose a biologically inspired neural network model which overcomes this problem. The proposed model consists of two distinct networks: one is a Hopfield type of chaotic associative memory and the other is a multilayer neural network. We consider that these networks correspond to the hippocampus and the neocortex of the brain, respectively. Information given is firstly stored in the hippocampal network with fast learning algorithm. Then the stored information is recalled by chaotic behavior of each neuron in the hippocampal network. Finally, it is consolidated in the neocortical network by using pseudopatterns. Computer simulation results show that the proposed model has much better ability to avoid catastrophic forgetting in comparison with conventional models.
Abstract: Functional near infrared spectroscopy (fNIRS) is a
practical non-invasive optical technique to detect characteristic of
hemoglobin density dynamics response during functional activation of
the cerebral cortex. In this paper, fNIRS measurements were made in
the area of motor cortex from C4 position according to international
10-20 system. Three subjects, aged 23 - 30 years, were participated in
the experiment.
The aim of this paper was to evaluate the effects of different motor
activation tasks of the hemoglobin density dynamics of fNIRS signal.
The chaotic concept based on deterministic dynamics is an important
feature in biological signal analysis. This paper employs the chaotic
properties which is a novel method of nonlinear analysis, to analyze
and to quantify the chaotic property in the time series of the
hemoglobin dynamics of the various motor imagery tasks of fNIRS
signal. Usually, hemoglobin density in the human brain cortex is
found to change slowly in time. An inevitable noise caused by various
factors is to be included in a signal. So, principle component analysis
method (PCA) is utilized to remove high frequency component. The
phase pace is reconstructed and evaluated the Lyapunov spectrum, and
Lyapunov dimensions. From the experimental results, it can be
conclude that the signals measured by fNIRS are chaotic.
Abstract: This paper presents a novel method for inferring the
odor based on neural activities observed from rats- main olfactory
bulbs. Multi-channel extra-cellular single unit recordings were done
by micro-wire electrodes (tungsten, 50μm, 32 channels) implanted in
the mitral/tufted cell layers of the main olfactory bulb of anesthetized
rats to obtain neural responses to various odors. Neural response
as a key feature was measured by substraction of neural firing rate
before stimulus from after. For odor inference, we have developed a
decoding method based on the maximum likelihood (ML) estimation.
The results have shown that the average decoding accuracy is about
100.0%, 96.0%, 84.0%, and 100.0% with four rats, respectively. This
work has profound implications for a novel brain-machine interface
system for odor inference.
Abstract: Verapamil has been shown to inhibit fentanyl uptake in vitro and is a potent P-glycoprotein inhibitor. Tissue partitioning of loperamide, a commercially available opioid, is closely controlled by the P-gp efflux transporter. The following studies were designed to evaluate the effect of opioids on verapamil partitioning in the lung and brain, in vivo. Opioid (fentanyl or loperamide) was administered by intravenous infusion to Sprague Dawley rats alone or in combination with verapamil and plasma, with lung and brain tissues were collected at 1, 5, 6, 8, 10 and 60 minutes. Drug dispositions were modeled by recirculatory pharmacokinetic models. Fentanyl slightly increased the verapamil lung (PL) partition coefficient yet decreased the brain (PB) partition coefficient. Furthermore, loperamide significantly increased PLand PB. Fentanyl reduced the verapamil volume of distribution (V1) and verapamil elimination clearance (ClE). Fentanyl decreased verapamil brain partitioning, yet increased verapamil lung partitioning. Also, loperamide increased lung and brain partitioning in vivo. These results suggest that verapamil and fentanyl may be substrates of an unidentified inward transporter in brain tissue and confirm that verapamil and loperamide are substrates of the efflux transporter P-gp.
Abstract: The frontal area in the brain is known to be involved in
behavioral judgement. Because a Kanji character can be discriminated
visually and linguistically from other characters, in Kanji character
discrimination, we hypothesized that frontal event-related potential
(ERP) waveforms reflect two discrimination processes in separate
time periods: one based on visual analysis and the other based
on lexcical access. To examine this hypothesis, we recorded ERPs
while performing a Kanji lexical decision task. In this task, either a
known Kanji character, an unknown Kanji character or a symbol was
presented and the subject had to report if the presented character was
a known Kanji character for the subject or not. The same response
was required for unknown Kanji trials and symbol trials. As a preprocessing
of signals, we examined the performance of a method
using independent component analysis for artifact rejection and found
it was effective. Therefore we used it. In the ERP results, there
were two time periods in which the frontal ERP wavefoms were
significantly different betweeen the unknown Kanji trials and the
symbol trials: around 170ms and around 300ms after stimulus onset.
This result supported our hypothesis. In addition, the result suggests
that Kanji character lexical access may be fully completed by around
260ms after stimulus onset.
Abstract: Brain Computer Interface (BCI) has been recently
increased in research. Functional Near Infrared Spectroscope (fNIRs)
is one the latest technologies which utilize light in the near-infrared
range to determine brain activities. Because near infrared technology
allows design of safe, portable, wearable, non-invasive and wireless
qualities monitoring systems, fNIRs monitoring of brain
hemodynamics can be value in helping to understand brain tasks. In
this paper, we present results of fNIRs signal analysis indicating that
there exist distinct patterns of hemodynamic responses which
recognize brain tasks toward developing a BCI. We applied two
different mathematics tools separately, Wavelets analysis for
preprocessing as signal filters and feature extractions and Neural
networks for cognition brain tasks as a classification module. We
also discuss and compare with other methods while our proposals
perform better with an average accuracy of 99.9% for classification.
Abstract: The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a level-sets approach to delineating three-dimensional brain tumors. Then we introduce a compression plan of 3D brain structures based for the meshes simplification, adapted for time to the specific needs of the telemedicine and to the capacities restricted by network communication. We present here the main stages of our system, and preliminary results which are very encouraging for clinical practice.
Abstract: Investigating language acquisition is one of the most
challenging problems in the area of studying language. Syllable
learning as a level of language acquisition has a considerable
significance since it plays an important role in language acquisition.
Because of impossibility of studying language acquisition directly
with children, especially in its developmental phases, computer
models will be useful in examining language acquisition. In this
paper a computer model of early language learning for syllable
learning is proposed. It is guided by a conceptual model of syllable
learning which is named Directions Into Velocities of Articulators
model (DIVA). The computer model uses simple associational and
reinforcement learning rules within neural network architecture
which are inspired by neuroscience. Our simulation results verify the
ability of the proposed computer model in producing phonemes
during babbling and early speech. Also, it provides a framework for
examining the neural basis of language learning and communication
disorders.
Abstract: Deep Brain Stimulation or DBS is the second solution
for Parkinson's Disease. Its three parameters are: frequency, pulse
width and voltage. They must be optimized to achieve successful
treatment. Nowadays it is done clinically by neurologists and there is
not certain numerical method to detect them. The aim of this research
is to introduce simulation and modeling of Parkinson's Disease
treatment as a computational procedure to select optimum voltage.
We recorded finger tremor signals of some Parkinsonian patients
under DBS treatment at constant frequency and pulse width but
variable voltages; then, we adapted a new model to fit these data. The
optimum voltages obtained by data fitting results were the same as
neurologists- commented voltages, which means modeling can be
used as an engineering method to select optimum stimulation
voltages.
Abstract: This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.
Abstract: Today, building automation is advancing from simple
monitoring and control tasks of lightning and heating towards more
and more complex applications that require a dynamic perception
and interpretation of different scenes occurring in a building. Current
approaches cannot handle these newly upcoming demands. In this
article, a bionically inspired approach for multimodal, dynamic scene
perception and interpretation is presented, which is based on neuroscientific
and neuro-psychological research findings about the perceptual
system of the human brain. This approach bases on data from diverse
sensory modalities being processed in a so-called neuro-symbolic
network. With its parallel structure and with its basic elements being
information processing and storing units at the same time, a very
efficient method for scene perception is provided overcoming the
problems and bottlenecks of classical dynamic scene interpretation
systems.
Abstract: Visual inputs are one of the key sources from which
humans perceive the environment and 'understand' what is
happening. Artificial systems perceive the visual inputs as digital
images. The images need to be processed and analysed. Within the
human brain, processing of visual inputs and subsequent
development of perception is one of its major functionalities. In this
paper we present part of our research project, which aims at the
development of an artificial model for visual perception (or
'understanding') based on the human perceptive and cognitive
systems. We propose a new model for perception from visual inputs
and a way of understaning or interpreting images using the model.
We demonstrate the implementation and use of the model with a real
image data set.
Abstract: Dynamic shear test on simulated phantom can be used
to validate magnetic resonance elastography (MRE) measurements.
Phantom gel has been usually utilized for the cell culture of cartilage
and soft tissue and also been used for mechanical property
characterization using imaging systems. The viscoelastic property of
the phantom would be important for dynamic experiments and
analyses. In this study, An axisymmetric FE model is presented for
determining the dynamic shear behaviour of brain simulated phantom
using ABAQUS. The main objective of this study was to investigate
the effect of excitation frequencies and boundary conditions on shear
modulus and shear viscosity in viscoelastic media.
Abstract: The way music is interpreted by the human brain is a very interesting topic, but also an intricate one. Although this domain has been studied for over a century, many gray areas remain in the understanding of music. Recent advances have enabled us to perform accurate measurements of the time taken by the human brain to interpret and assimilate a sound. Cognitive computing provides tools and development environments that facilitate human cognition simulation. ACT-R is a cognitive architecture which offers an environment for implementing human cognitive tasks. This project combines our understanding of the music interpretation by a human listener and the ACT-R cognitive architecture to build SINGER, a computerized simulation for listening and recalling songs. The results are similar to human experimental data. Simulation results also show how it is easier to remember short melodies than long melodies which require more trials to be recalled correctly.
Abstract: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
Abstract: In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
Abstract: In order to answer the general question: “What does a simple agent with a limited life-time require for constructing a useful representation of the environment?" we propose a robot platform including the simplest probabilistic sensory and motor layers. Then we use the platform as a test-bed for evaluation of the navigational capabilities of the robot with different “brains". We claim that a protocognitive behavior is not a consequence of highly sophisticated sensory–motor organs but instead emerges through an increment of the internal complexity and reutilization of the minimal sensory information. We show that the most fundamental robot element, the short-time memory, is essential in obstacle avoidance. However, in the simplest conditions of no obstacles the straightforward memoryless robot is usually superior. We also demonstrate how a low level action planning, involving essentially nonlinear dynamics, provides a considerable gain to the robot performance dynamically changing the robot strategy. Still, however, for very short life time the brainless robot is superior. Accordingly we suggest that small organisms (or agents) with short life-time does not require complex brains and even can benefit from simple brain-like (reflex) structures. To some extend this may mean that controlling blocks of modern robots are too complicated comparative to their life-time and mechanical abilities.
Abstract: The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.