Berdakh Abibullaev

A New QRS Detection Method Using Wavelets and Artificial Neural Networks

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.

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

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.

Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms

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.

Neural Network Classification of Brain Hemodynamic Responses from Four Mental Tasks

We investigate subjects’ brain hemodynamic activities during mental tasks using a nearinfrared spectroscopy. A wavelet and neural network-based methodology is presented for recognition of brain hemodynamic responses. The recognition is performed by a single layer neural network classifier according to a backpropagation algorithm with two error minimizing techniques. The performance of the classifier varied depending on the neural network model, but the performance was usually at least 90%. The classifier usually converged faster and attained a somewhat greater level of performance when an input was presented with only relevant features. The overall classification rate was higher than 94%. The study demonstrates the accurate classifiablity of human brain hemodynamic useful in various brain studies.

EPILEPTIC SPIKE DETECTION USING CONTINUOUS WAVELET TRANSFORMS AND ARTIFICIAL NEURAL NETWORKS

We propose a new method for detection and classification of noisy recorded epileptic transients in Electroencephalograms (EEG) using the continuous wavelet transform (CWT) and artificial neural networks (ANN). The proposed method consists of a segmentation, feature extraction and classification stage. For the feature extraction stage, we use best basis mother wavelet functions and wavelet thresholding technique. For the classification stage, multilayer perceptron neural networks were implemented according to standard backpropagation learning formulations. We demonstrate the efficiency of our feature extraction method on data to improve the ANN detection performance. As a result, we achieved the accuracy in detection and classification of seizure EEG signals with 94.69%, which is relatively good comparing with the available algorithms at present time.

The beginning of neurohaptics: Controlling cognitive interaction via brain haptic interface

This study showed an example of neurohaptic interface which can be the direct connector between haptics and brain. We investigated the neural activities of motion which are essential tasks for haptic interaction. Eating was adopted to explore the neural activities from the functional near-infrared spectroscopy (fNIRS) imaging. Subjects carried out real motion, action observation, and motor imagery. From this study we convinced that the action observation and motor imagery may create the similar neural activities to the active movement. In addition, we showed the feasibility of an fNIRS integrated brain haptic interaction based on the neural mechanism of action observation. Implications of this study suggested that the neurohaptics can play a more active role in realistic haptic interaction for the real applications including brain computer interface.

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

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.

EEG Source Imaging in Partial Epilepsy in Comparison with Presurgical Evaluation and Magnetoencephalography

Background and Purpose 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). Methods The epilepsy syndrome of 27 partial epilepsy patients was determined by presurgical evaluations. EEG recordings were made using 70 scalp electrodes, and the 3D coordinates of the electrodes were digitized. ESI images of individual and averaged spikes were analyzed by Curry software with a boundary element method. MEG and ECoG were performed in 23 and 9 patients, respectively. Results ESI and MEG source imaging (MSI) results were well concordant with the results of presurgical evaluations (in 96.3% and 100% cases for ESI and MSI, respectively) at the lobar level. However, there were no spikes in the MEG recordings of three patients. The ESI results were well concordant with MSI results in 90.0% of cases. Compared to ECoG, the ESI results tended to be localized deeper than the cortex, whereas the MSI results were generally localized on the cortical surface. ESI was well concordant with ECoG in 8 of 9 (88.9%) cases, and MSI was also well concordant with ECoG in 4 of 5 (80.0%) cases. The EEG single dipoles in one patient with mesial temporal lobe epilepsy were tightly clustered with the averaged dipole when a 3 Hz high-pass filter was used. Conclusions The ESI results were well concordant with the results of the presurgical evaluation, MSI, and ECoG. The ESI analysis was found to be useful for localizing the seizure focus and is recommended for the presurgical evaluation of intractable epilepsy patients.

Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot

With the development of Brain-Computer Interface (BCI) systems people with motor disabilities are able to control external devices using their thoughts. To control a device through BCI, brain activities of the user must be accurately translated to meaningful commands and a design of appropiate BCI paradigms play important roles in such tasks. This work presents a design and evaluation of a BCI system that is based on P300 Event-Related Potentials (ERP) in order to control a mobile robot platform into four directions (left, right, front, back). The ultimate goal of this research is to provide convienient way of controlling a mobile robot as an assistive home technology for disabled people. Low cost EPOC Emotiv headset was used in the BCI system to acquire brain signals with a Jaguar 4x4 Wheel robot as a control platform. We discuss a set of signal processing steps employed in detail and the utility of a regularized logistic regression classifier to detect visual stimuli induced P300 ERPs and, to control the Jaguar robot.