Search results for "recognition"
showing 10 items of 3607 documents
Measuring Functional Connectivity of Human Intra-Cortex Regions with Total Correlation
2021
The economy of brain organization makes the primate brain consume less energy but efficiency. The neurons densely wired each other dependent on both anatomy structure connectivity and functional connectivity. Here, I only describe functional connectivity with Functional Magnetic Resonance Imaging (fMRI) data. Most importantly, how to quantitative measure information share or separate among functional brain regions, what’s worse, fMRI data exist large dimensional problems or “curse dimensionality” [1]. However, the multivariate total correlation method can perfectly address the above problems. In this paper, two things measured with the information-theoretic technique - total correlation [2,…
Individual Independent Component Analysis on EEG: Event-Related Responses Vs. Difference Wave of Deviant and Standard Responses
2016
Independent component analysis (ICA) is often used to spatially filter event-related potentials (ERPs). When an oddball paradigm is applied to elicit ERPs, difference wave (DW, responses of deviant stimuli minus those of standard ones) is often used to remove the common responses between the deviant and the standard. Thus, DW can be produced first, and then ICA is used to decompose the DW. Or, ICA is performed on responses of the deviant and standard stimuli separately, and then DW is applied on the filtered responses. In this study, we compared the two approaches to analyzing mismatch negativity (MMN). We found that DW introduced noise in the time and space domains, resulting in more diffi…
Clustering Algorithms for MRI
1991
Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to perform a preliminary segmentation of the image; it can guide the final manual classification of body-tissues. Three clustering techniques and their integration in a MRI-system are described. Their performance and accuracy was evaluated on synthetic and real image-data. A comparison of our approach with the tissue-classification done by a rad…
Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks
2019
Deep learning has contributed greatly to functional magnetic resonance imaging (fMRI) analysis, however, spatial maps derived from fMRI data by independent component analysis (ICA), as promising biomarkers, have rarely been directly used to perform individualized diagnosis. As such, this study proposes a novel framework combining ICA and convolutional neural network (CNN) for classifying schizophrenia patients (SZs) and healthy controls (HCs). ICA is first used to obtain components of interest which have been previously implicated in schizophrenia. Functionally informative slices of these components are then selected and labelled. CNN is finally employed to learn hierarchical diagnostic fea…
Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
2018
Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ dat…
Functional Near Infrared Spectroscopy System Validation for Simultaneous EEG-FNIRS Measurements
2019
Functional near-infrared spectroscopy (fNIRS) applied to brain monitoring has been gaining increasing relevance in the last years due to its not invasive nature and the capability to work in combination with other well–known techniques such as the EEG. The possible use cases span from neural-rehabilitation to early diagnosis of some neural diseases. In this work a wireline FPGA–based fNIRS system, that use SiPM sensors and dual-wavelength LED sources, has been designed and validated to work with a commercial EEG machine without reciprocal interference.
Empirical Mode Decomposition on Mismatch Negativity
2008
Empirical mode decomposition (EMD) has been applied in the various disciplines to extract the desired signal. The basic principle is to decompose a time series into intrinsic mode functions (IFMs) and each IFM corresponds to an oscillation phenomenon. A statistical description of the oscillatory activities of the EEG has been well known. It is desired to extract single oscillatory process from the EEG by EMD. Mismatch negativity (MMN) can be automatically elicited by the deviant stimulus in an oddball paradigm, in which physically the deviant stimulus occurs among repetitive and homogeneous stimuli. MMN thus reflects the ability of the brain to detect changes in auditory stimuli. So, the MM…
Extract Mismatch Negativity and P3a through Two-Dimensional Nonnegative Decomposition on Time-Frequency Represented Event-Related Potentials
2010
This study compares the row-wise unfolding nonnegative tensor factorization (NTF) and the standard nonnegative matrix factorization (NMF) in extracting time-frequency represented event-related potentials—mismatch negativity (MMN) and P3a from EEG under the two-dimensional decomposition The criterion to judge performance of NMF and NTF is based on psychology knowledge of MMN and P3a MMN is elicited by an oddball paradigm and may be proportionally modulated by the attention So, participants are usually instructed to ignore the stimuli However the deviant stimulus inevitably attracts some attention of the participant towards the stimuli Thus, P3a often follows MMN As a result, if P3a was large…
Effectiveness of tDCS at Improving Recognition and Reducing False Memories in Older Adults
2021
Background: False memories tend to increase in healthy and pathological aging, and their reduction could be useful in improving cognitive functioning. The objective of this study was to use an active–placebo method to verify whether the application of transcranial direct current stimulation (tDCS) improved true recognition and reduced false memories in healthy older people. Method: Participants were 29 healthy older adults (65–78 years old) that were assigned to either an active or a placebo group
Ocular counterrolling. Some practical considerations of a new evaluation method for diagnostic purposes.
1986
Ocular counterrolling (OCR) data taken from the literature (12 publications) were used to test the best fit (least-square fit) of these measurements with respect to three mathematical models: a sine relation between OCR and the lateral tilt stimulus, a complex cosine-square relation, and a logarithmic relation between OCR gain and tilt. The latter proved to be the best fitting function. On the basis of this model, we attempted to define a physiological transfer function between OCR gain and tilt, which could serve as a reference of normal population, assuming healthy subjects for the investigations applied. Comparison of this physiological range with pathological data demonstrated marked di…