a-novel-experimental-analytical-approach-to-multimodal-neural

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental ...

University of Houston

We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.

A new QRS detection method using wavelets and artificial neural networks.

In this paper, we propose a novel method using best basis wavelet functions and double thresholding that are well suited for detecting and localization of important epileptic events from noisy recorded seizure EEG signals. Our technique is based on dyadic wavelet decomposition and is mainly concerned detection of single epileptic transients within the observation sequence, such as ictal and interictal epochs of EEG. In our experiment we use temporal lobe epileptic data recorded during 84 h from four patients diagnosed with epilepsy. We have achieved promising results that demonstrate efficiency and simplicity that can be used in clinical studies as an automatic decision support tool. Thus reduce the physician's workload and provide accurate diagnosis of epileptic seizures.

Seizure detection in temporal lobe epileptic EEGs using the best basis wavelet functions.

Attention deficit hyperactivity disorder is a complex brain disorder which is usually difficult to diagnose. As a result many literature reports about the increasing rate of misdiagnosis of ADHD disorder with other types of brain disorder. There is also a risk of normal children to be associated with ADHD if practical diagnostic criteria are not supported. To this end we propose a decision support system in diagnosing of ADHD disorder through brain electroencephalographic signals. Subjects of 10 children participated in this study, 7 of them were diagnosed with ADHD disorder and remaining 3 children are normal group. Our main goal of this sthudy is to present a supporting diagnostic tool that uses signal processing for feature selection and machine learning algorithms for diagnosis.Particularly, for a feature selection we propose information theoretic which is based on entropy and mutual information measure. We propose a maximal discrepancy criterion for selecting distinct (most distinguishing) features of two groups as well as a semi-supervised formulation for efficiently updating the training set. Further, support vector machine classifier trained and tested for identification of robust marker of EEG patterns for accurate diagnosis of ADHD group. We demonstrate that the applicability of the proposed approach provides higher accuracy in diagnostic process of ADHD disorder than the few currently available methods.

Decision support algorithm for diagnosis of ADHD using electroencephalograms.

Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.

Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms.

Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.

Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning.

Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method.

Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: an fNIRS pilot study.

The aim of this study was to determine the usefulness of three-dimensional (3D) scalp EEG source imaging (ESI) in partial epilepsy in comparison with the results of presurgical evaluation, magnetoencephalography (MEG), and electrocorticography (ECoG).

Berdakh Abibullaev

Laboratory for Noninvasive Brain-Machine Interface Systems,
Department of Electrical and Computer Engineering,
Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering

University of Houston

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Berdakh Abibullaev

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Laboratory for Noninvasive Brain-Machine Interface Systems,Department of Electrical and Computer Engineering,Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering

University of Houston

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Laboratory for Noninvasive Brain-Machine Interface Systems,
Department of Electrical and Computer Engineering,
Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering