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Seung Hyun Lee,Berdakh Abibullaev1,Won-Seok Kang,Yunhee Shin,Jinung An 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
This paper presents our preliminary study EEG brain signals of children with attention deficit hyperactivity disorder (ADHD) in order to support a computer assisted diagnostic system. The EEG signals were recorded from 13 children including normal and children diagnosed with ADHD. We analyzed the signals with multilevel discrete wavelet decompositions in order to extract brain signal power spectrum features. A wavelet thresholding technique was employed to further improve the data quality by denoising the artifacts. In order to discriminate the attention level in electrical brain activity of ADHD children, we used a standard Self-Organizing Map clustering technique with wavelet coefficient input features. Clustering results varied depending on the wavelet feature extraction stage, particularly it was noticed that accuracy was dependent on the type of the used wavelet function. The clustering results demonstrate that ‘sym7’ wavelet function provides better input feature localization to provide the accurate separation of normal and disordered children’s brain activity.
Abibullaev, Berdakh,An, Jinung,Lee, Seung Hyun,Moon, Jeon Il Elsevier 2017 Measurement Vol.98 No.-
<P><B>Abstract</B></P> <P>The integration of Brain-Computer-Interfaces (BCI) into rehabilitation research is a promising approach that may substantially impact the rehabilitation success. Yet, there is still significant challenges that needs to be addressed before the BCI technology can be fully used effectively in a clinical setting as a neural prosthesis for motor impaired users. As it is still unknown whether the conventional BCI induction strategies that use different the types of stimuli and/or mental tasks induce cortical reorganization for disabled users. This paper presents a design and evaluation of a real-time Near-Infrared Spectroscopy (NIRS) based BCI protocol to control an external haptic device, and an interesting source of brain signals that may convey complementary information for inducing neuroplasticity. The protocol is based on the ideas derived from Mirror-based Therapy (MT) in which subjects not only perform literal motor imagery tasks but also combine their intents with visual action observation of a related motor imagery task. The NIRS-BCI system then commands a haptic device in real-time to move in opposing directions of leftward and rightward movement. We also compare the proposed protocol to the conventional limb motor imagery task and verify its efficacy with online decoding accuracies up to 94.99%. The initial validation of the experimental setup was done with seven healthy subjects. Nonetheless we contend that the design of the current NIRS-BCI method hold promise with patient populations for effective stroke rehabilitation therapy, because the beneficial effects of MT alone in post-stroke recovery has already been manifested in the literature.</P>
Decision support algorithm for diagnosis of ADHD using electroencephalograms.
Abibullaev, Berdakh,An, Jinung Kluwer Academic/Plenum Publishers 2012 JOURNAL OF MEDICAL SYSTEMS Vol.36 No.4
<P>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.</P>
Analysis of Epileptic Seizures in EEG using Wavelet Transforms
Abibullaev Berdakh,Seo Hee-Don,Kim Min-Soo 대한전자공학회 2008 ICEIC:International Conference on Electronics, Inf Vol.1 No.1
We propose a novel method for the detection and localizing of noisy recorded epileptic transients using continuous time wavelet transform, by employing the best basis wavelet functions. We demonstrate the efficiency of the method on data to identify and clearly locate in time the seizure epochs such as preseizure, seizure and activities post seizure. It shows that our method is superior both in separation from noise and in identifying superimposed epileptic action potentials. This proposed best basis wavelet function method provides a significant improvement under extremely low signal to noise ratios and low spike firing rate, a situation commonly found in actual experiments.
Recognition of Brain Hemodynamic Mental Response for Brain Computer Interface
Berdakh Abibullaev,Won-Seok Kang,Seung-Hyun Lee,Jinung An 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
Recent advances in neuroimaging demonstrate the potential use of functional near infrared spectroscopy (fNIRS) in the field of brain machine interface. An fNIRS uses light in the near infrared range to measure brain surface hemoglobin concentrations to determine a neural activity. The current study presents our empirical results in realizing fNIRS ?- BCI system. We analyze the hemodynamic responses that are acquired from 4 subjects’" frontal cortex using 19-channel fNIRS recordings. A wavelet-neural network methodology is proposed in this study, in order to extract important neural features and to recognize the cognitive tasks. Experimental results demonstrate the potential application of fNIRS for BCI by confirming the best accuracy rate as high as 97% in recognizing the different levels of cognitive tasks. Particularly, we demonstrate efficient way of extracting cognitive neural features by wavelet pre-processing and optimal neural network classifier.
Characteristic wave detection in ECG using complex-valued Continuous Wavelet Transforms
Berdakh, Abibullaev,Seo, Hee-Don The Korean Society of Medical and Biological Engin 2008 의공학회지 Vol.29 No.4
In this study the complex-valued continuous wavelet transform (CWT) has been applied in detection of Electrocardiograms (ECG) as response to various signal classification methods such as Fourier transforms and other tools of time frequency analysis. Experiments have shown that CWT may serve as a detector of non-stationary signal changes as ECG. The tested signal is corrupted by short time events. We applied CWT to detect short-time event and the result image representation of the signal has showed us that one can easily find the discontinuity at the time scale representation. Analysis of ECG signal using complex-valued continuous wavelet transform is the first step to detect possible changes and alternans. In the second step, modulus and phase must be thoroughly examined. Thus, short time events in the ECG signal, and other important characteristic points such as frequency overlapping, wave onsets/offsets extrema and discontinuities even inflection points are found to be detectable. We have proved that the complex-valued CWT can be used as a powerful detector in ECG signal analysis.
ANALYSIS OF EEG SIGNALS BY THE CONTINUOUS WAVELET TRANSFORM
Hee-Don Seo,Berdakh Abibullaev,Min-Soo Kim 한국로고스경영학회 2007 한국로고스경영학회 학술발표대회논문집 Vol.2007 No.7월_2
We propose the analysis of the sleep EEG, the forecasting of epileptic seizures from the EEG signal, and the classification of EEG signal during mental tasks using BCI system. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The mental tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. In BCI system, the architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved 95% classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks, And this research can be reduce doctor's labors and realize quantitative diagnosis of EEG. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.