Gait Analysis
Ali Maleki; Elham Hasani
Volume 16, Issue 3 , December 2022, , Pages 217-228
Abstract
Parkinson's disease is a neurodegenerative disease that causes severe movement disorders including bradykinesia, rigidity, and tremors. There is no cure for Parkinson's disease, only the symptoms can be managed. Parkinson's disease is diagnosed using the MDS-UPDRS global grading scale. In this scale, ...
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Parkinson's disease is a neurodegenerative disease that causes severe movement disorders including bradykinesia, rigidity, and tremors. There is no cure for Parkinson's disease, only the symptoms can be managed. Parkinson's disease is diagnosed using the MDS-UPDRS global grading scale. In this scale, four levels including slight, mild, moderate, and severe levels are defined for the disease. Recurrence plots and RQA features are tools for describing the behavior of chaotic systems and revealing hidden patterns in system dynamics. In this paper, the effect of Parkinson's disease progression on RQA chaotic features is studied. For this purpose, the dataset of the accelerometer mounted on the hand during the finger tapping test was used, which included 67 healthy data, 54 level one data, 66 level two data, 59 level three data, and 14 level four data. After pre-processing, the recurrence plots of the data were drawn and their RQA characteristics were calculated. Patterns of recurrence plots including separate recurrence points, diagonal lines, vertical lines, black squares, and horizontal and vertical white bands were investigated. According to the obtained results, the patterns of recurrence plots had significant differences among different levels of Parkinson's disease. Therefore, RQA features can be used to automatically determine the level of Parkinson's disease.
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Ali Maleki; Maedeh Azadimoghadam
Volume 16, Issue 3 , December 2022, , Pages 229-240
Abstract
A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. ...
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A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. The first step to studying and ultimately reducing the destructive effects of fatigue is to identify the level of fatigue. Although frequency indices have been used for fatigue evaluation, the results of previous research in this field are inconsistent. Therefore, there is no detailed and comprehensive investigation of how fatigue affects frequency indices. In this paper, the evaluation of frequency-domain fatigue indicators has been done accurately and comprehensively. For this purpose, nine visual stimuli with different flickering frequencies were displayed to the subject, and they were asked to pay attention to the target cue. The visual stimulation was presented continuously, without rest to ensure that the fatigue occurs at the end of the test. Mean amplitude of theta, alpha, and beta bands, and 4-30Hz frequency band segments with 1Hz, 2Hz, and 4Hz steps were evaluated as fatigue indices. The results show that the mean amplitude of the frequency band of 8-9 Hz is more suitable for fatigue evaluation. This index has the most changes with fatigue in a state of wakeful relaxation of the subject and the mental effort to maintain the level of alertness in the fatigue state.
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Marzie Alirezaei Alavijeh; Ali Maleki
Volume 16, Issue 1 , May 2022, , Pages 1-9
Abstract
Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such ...
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Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such systems. This paper uses the EMD algorithm in the initial phase and CCA or LASSO for the recognition of the stimulation frequency. In the first step, the EMD algorithm is applied so that non-stationary SSVEP signal breaks into oscillating functions and meaningful information are extracted. Among the IMFs obtained from the EMD method, only IMFs whose amplitude of the frequency spectrum in the frequency ranges corresponding to the excitation is higher were selected. With this selection, noisy signals and unprofitable information can be omitted. In the proposed method, two CCA and LASSO diagnostic methods were performed on the sum of selected signals to identify the frequency of stimulation. The simulation results show the recognition accuracy of 81.76% and 82.26% for the proposed method EMD-CCA and EMD-LASSO, respectively. While detection accuracy is 78.10% and 78.72% for conventional methods of CCA and LASSO.
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Sepide Khoneiveh; Ali Maleki
Volume 12, Issue 2 , September 2018, , Pages 161-171
Abstract
Steady state somatosensory evoked potential (SSSEP) is one of the control signals of brain-computer interfaces (BCI), based on the reflection of skin vibrational stimulation with specific frequencies in brain signals. BCI systems based on SSSEP do not cause visual fatigue in comparison with SSVEP based ...
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Steady state somatosensory evoked potential (SSSEP) is one of the control signals of brain-computer interfaces (BCI), based on the reflection of skin vibrational stimulation with specific frequencies in brain signals. BCI systems based on SSSEP do not cause visual fatigue in comparison with SSVEP based BCI systems, and they can be used for locked-in or amyotrophic lateral sclerosis (ALS) patients. So far, few studies have been done on SSSEP and its applications in BCI systems, because the hardware implementation of this system is challenging. In this paper, a vibrational stimulation device based on vibrational motor has been developed. This device has two separate output channels for applying vibrational stimulation to two different points of the body. The output frequency of each channel is adjustable in the range of 15 to 35 Hz with a step of 1 Hz. All parts of the device and the actuators have been shielded to prevent the emission of electromagnetic noise.
Human Computer Interaction / HCI
Sahar Sadeghi; Ali Maleki
Volume 11, Issue 2 , June 2017, , Pages 101-109
Abstract
To increase the number of stimulation frequencies in the Steady-state visual evoked potential (SSVEP)-based brain-computer interface, we are forced to broaden the frequency range due to the frequency resolution restriction. This will enter frequencies with harmonic relation into the stimulation frequency ...
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To increase the number of stimulation frequencies in the Steady-state visual evoked potential (SSVEP)-based brain-computer interface, we are forced to broaden the frequency range due to the frequency resolution restriction. This will enter frequencies with harmonic relation into the stimulation frequency range and lead to increase in frequency recognition error. In this paper, a three-stage method including the empirical mode decomposition (EMD), the canonical correlation analysis (CCA) and neural network classifier has been proposed that can solve the recognition error problem for wide frequency range including frequencies with harmonic relation. Visual stimulus ranged from 6-16 Hz with an interval of 0.5 have been generated using Matlab and the psychophysics toolbox. The SSVEP signal was recorded from ten subjects via one electrode placed at Oz. After extracting the intrinsic mode functions (IMFs) of the signal by EMD and reconstructing the combined signals, the CCA has been applied. Two features including the detected frequency and the correlation value in this frequency have been extracted and they were given to the neural network classifier. For eight-second time window, the average accuracy of the CCA for N=1 was 78% and for N=2 was 74%, while the corresponding values of the proposed method were 82% and 77% respectively. For four-second time window, the accuracy was increased from 78% to 83% for N=1 and it was increased from 78% to 80% for N=2. N is the number of harmonics in the generation of the reference signal in the CCA. For wide frequency range, the proposed method has been able to improve the frequency recognition accuracy compared to the standard CCA method. according to this, by broadening the stimulation frequency range, the possibility of increasing the number of frequency options and thus increasing the information transfer rate are provided.
Bioelectrics
Marzieh Alirezaei Alavijeh; Ali Maleki
Volume 10, Issue 2 , August 2016, , Pages 187-196
Abstract
Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also ...
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Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also an important criterion to have a system that is applicable in real life and have the ability to use online. In this paper, a method based on standard CCA have been present for recognition of stimulus frequency. The proposed method is performed in two stages, offline and online. In the offline stage, the standard CCA is applied to the SSVEP and sin-cos reference signals. After that, template signals are constructed using weights that generate maximum correlation. In online stage, cross correlation between test signal and each template signals are calculated and the stimulus frequency is recognized. The greater accuracy of frequency recognition and less calculation time at the same time are shown by stimulation result.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mina Hemmatian; Ali Maleki
Volume 9, Issue 2 , July 2015, , Pages 163-178
Abstract
The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion ...
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The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion to evaluate chaotic systems and to quantify the fractal dimensions. Previous studies have shown that classical Hurst index is not appropriate for classification of cardiac arrhythmias because not only selection of algorithm parameters affect the value of determined Hurst index, but also it significantly varies as the heart rate changes. In this paper, modified multiple Hurst index has been proposed to classify the cardiac arrhythmias. The presented index is resistant against changes in heart rate and can be used to identify appropriate features to classify the cardiac arrhythmias. 80 signal from four types of ECG beats obtained from the MIT-BIH Arrhythmia dataset has been used to validate the algorithm. Results show that this method is able to detect normal rhythm and right bundle branch block (RBBB), left bundle branch block (LBBB) and atrial premature complex (APC) arrhythmias with accuracy of 100%, 96.25% and 88.75% using artificialneural network, k nearest neighbor and LDA classifiers respectively.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Afarin Nazemi; Ali Maleki
Volume 8, Issue 4 , February 2015, , Pages 411-420
Abstract
Classification of distal limb movements based on surface electromyography (sEMG) of proximal muscles is an important issue in the control of myoelectric hand prosthesis. In most of previous studies, classification of a limited number of hand motions is investigated. In this paper, we have used NINAPRO ...
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Classification of distal limb movements based on surface electromyography (sEMG) of proximal muscles is an important issue in the control of myoelectric hand prosthesis. In most of previous studies, classification of a limited number of hand motions is investigated. In this paper, we have used NINAPRO database containing kinematics and sEMG of upper limbs while performing 52 finger, hand and wrist movements. We evaluated performance of LDA and LS-SVM with RBF kernel classifiers using different combination of features. First by windowing the signal with two different methods, the major part of the signal was selected and eight various temporal features (MAV, IAV, RMS, WL, E, ER1, ER2, CC) were extracted. Then performance of each classifier with single, double and multiple combinations of features was evaluated. For LDA classifier, the best average classification accuracy of 84.23% was achived for first windowing method and MAV (or IAV)+CC features, The corresponding accuracy for LS-SVM classifier with second windowing method and IAV+MAV+RMS+WL features, was 85.19%.
