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A 13µW 87dB dynamic range implantable ΔΣ modulator for full-spectrum neural recording
(IEEE, 2013-09-26) Jian, Xu; Islam, Md Kafiul; Wang, Shuo; Yang, Zhi
Experiment analysis on in-vivo data sequences suggests a wide system dynamic range (DR) is required to simultaneously record local field potentials (LFPs), extra-cellular spikes, and artifacts/interferences. In this paper, we present a 13 μW 87 dB DR ΔΣ modulator for full-spectrum neural recording. To achieve a wide DR and low power consumption, a fully-differential topology is used with multi-bit (MB) quantization scheme and switched-opamp (SO) technique. By adopting a novel fully-clocked scheme, a power-efficient current-mirror SO is developed with 50% power saving, which doubles the figure-of-merit (FOM) over its counterpart. A new static power-less multi-bit quantizer with 96% power and 69% area reduction is also introduced. Besides, instead of metal-insulator-metal (MIM) capacitor, three high-density MOS capacitor (MOSCAP) structures are employed to reduce circuit area. Measurement results show a peak signal-to-noise and distortion ratio (SNDR) of 85 dB with 10 kHz bandwidth at 1.0 V supply, corresponding to an FOM of 45 fJ/conv.-step. which is implemented in a 0.18 μm CMOS.
Artifact Characterization and Removal for In-Vivo Neural Recording
(Elsevier, 2014-04-15) Islam, Md Kafiul; Rastegarnia, Amir; Nguyen, Anh Tuan; Yang, Zhi
Background: In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest. New method: The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed. Results: Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio. Comparison with existing methods: Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms. Conclusion: Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments
The Journey of Elastography: Background, Current Status and Future Possibilities in Breast Cancer Diagnosis
(Elsevier, 2015) Faruk, Tasnuva; Islam, Md Kafiul; Arefin, Sams; Haq, Md Zahurul
Elastography is a promising way to assess tissue differences regarding stiffness or elasticity for what was historically assessed manually by palpation. Combined with conventional imaging modalities (eg, ultrasonography [US]), elastography can potentially evaluate the stiffness of a breast lesion and consequently help to detect malignant breast tumor from benign ones. Recent studies show that ultrasonographic elastography (USE) provides higher image quality compared with conventional B-mode US or mammography during breast cancer diagnosis, which eventually helps to reduce false-positive results (ie, increased specificity) and therefore is useful in avoiding breast biopsy. This article reviews the basics of elastography technique, classifications, diagnosis results obtained from clinical studies to date for differentiating malignant breast tumors from benign lesions, and its future possibilities. In addition, this article generalizes different elastography methods, modes, and associated imaging modalities in a simpler way and attempts to identify misconceptions and confusion related to existing elastography techniques. It also makes an effort to identify the gaps of information that need to be filled so that interested researchers can get an overall idea of elastography-based methods in a convenient way to carry out their research on breast elastography for prospective future applications, e.g. breast cancer diagnosis or even in intraoperative breast tumor localization.
A Wavelet-Based Artifact Reduction from Scalp EEG for Epileptic Seizure Detection
(IEEE, 2015-07-15) Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi
This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.
Frequency Estimation of Unbalanced Three-Phase Power Systems Using the Modified Adaptive Filtering
(Scientific & Academic Publishing, 2015-08) Rastegarnia, Amir; Khalili, Azam; Vahidpour, Vahid; Islam, Md Kafiul
In this paper, the problem of frequency estimation using adaptive filters is addressed based on the augmented complex normalized least mean squares (ACNLMS) technique. In other words, motivated from ACLMS technique, a new version called ACNLMS that outperforms the previous techniques is proposed. The proposed method makes use of two weight coefficients, like ACLMS, together with normalized time variant step size, thus its promising in increasing and enhancing both convergence rate and accuracy. Then, using derived weight coefficient, the frequency is estimated at each step. The performance and convergence analysis of the proposed method along with simulation results comparing with two existing techniques, CLMS and ACLMS, are provided in critical cases such as various unbalanced conditions and presence of harmonic distortion. Simulation results indicate that the proposed technique achieves a better performance in terms of convergence rate and frequency estimation accuracy as compared with CLMS and ACLMS techniques. Moreover, ACLMS achieves a smaller error variance than the other mentioned cases.
A High Performance Delta-Sigma Modulator for Neurosensing
(Sensors, 2015-08-07) Jian, Xu; Zhao, Menglian; Wu, Xiaobo; Islam, Md Kafiul; Yang, Zhi
Recorded neural data are frequently corrupted by large amplitude artifacts that are triggered by a variety of sources, such as subject movements, organ motions, electromagnetic interferences and discharges at the electrode surface. To prevent the system from saturating and the electronics from malfunctioning due to these large artifacts, a wide dynamic range for data acquisition is demanded, which is quite challenging to achieve and would require excessive circuit area and power for implementation. In this paper, we present a high performance Delta-Sigma modulator along with several design techniques and enabling blocks to reduce circuit area and power. The modulator was fabricated in a 0.18-μm CMOS process. Powered by a 1.0-V supply, the chip can achieve an 85-dB peak signal-to-noise-and-distortion ratio (SNDR) and an 87-dB dynamic range when integrated over a 10-kHz bandwidth. The total power consumption of the modulator is 13 μW, which corresponds to a figure-of-merit (FOM) of 45 fJ/conversion step. These competitive circuit specifications make this design a good candidate for building high precision neurosensors.
Artifact Characterization, Detection and Removal from Neural Signals
(National University of Singapore, 2015-12) Islam, Md Kafiul; Zhi Yang
Artifact detection and removal are important preprocessing steps for neural recordings to decode the neural signals properly and currently an active research problem. In this thesis, for the first time, artifacts found in the in-vivo neural recordings are studied and consequently a systematic artifact characterization is presented. Subsequently, three different artifact removal methods are proposed (one for in-vivo neural signals in general application and two for EEG data for two different application purposes, i.e. seizure detection and brain-computer interface (BCI) experiments). In order to evaluate the proposed methods quantitatively in comparison with available state-of-the-art methods, different artifact types have been extracted from real recordings for simulating artifact templates and hence to build a synthesized neural database on which the methods are applied. In addition, the effects of artifact removal on the later-stage processing have also been evaluated resulting significant performance improvement which proofs the efficacy of the proposed methods.
A Self-Organizing Diffusion Mobile Adaptive Network for Pursuing a Target
(Scientific & Academic Publishing, 2016) Rastegarnia, Amir; Khalili, Azam; Islam, Md Kafiul
In this paper we focus on designing self-organizing diffusion mobile adaptive networks where the individual agents are allowed to move in pursuit of an target. The well-known Adapt-then-Combine (ATC) algorithm is already available in the literature as a useful distributed diffusion-based adaptive learning network. However, in the ATC diffusion algorithm, fixed step sizes are used in the update equations for velocity vectors and location vectors. When the nodes are too far away from the target, such strategies may require large number of iterations to reach the target. To address this issue, we suggest two modifications on the ATC mobile adaptive network to improve its performance. The proposed modifications include (i) distance-based variable step size adjustment at diffusion algorithms to update velocity vectors and location vectors, (ii) to use a selective cooperation, by choosing the best nodes at every iteration, to reduce the number of communications. The performance of the proposed algorithm is evaluated by simulation tests where the obtained results show the superior performance of the proposed algorithm in comparison with the available ATC mobile adaptive network.
Steady-State Tracking Analysis of Adaptive Filter With Maximum Correntropy Criterion
(Springer, 2016-08-03) Khalili, Azam; Rastegarnia, Amir; Islam, Md Kafiul; Rezaii, Tohid Yousefi
This letter studies the tracking performance of a stochastic gradient-based adaptive algorithm, namely the maximum correntropy criterion algorithm, where a random walk is used to model the non-stationarity. In our analysis, we use the energy conservation argument to derive expressions for the steady-state excess mean square error (EMSE). We consider two different cases for measurement of noise distribution including the Gaussian noise and general non-Gaussian noise. For the Gaussian case, we derive a fixed-point equation that can be solved numerically to find steady-state EMSE value. For the general non-Gaussian case, we derive an approximate closed-form expression for EMSE. For both cases, unlike the stationary environment, the EMSE curves are not increasing functions of step size parameter. We use this observation to find the optimum step size learning parameter for general non-Gaussian case. The validity of the theoretical results are justified via simulation results.
Methods for Artifact Detection and Removal from Scalp EEG: A Review
(Elsevier, 2016-10-15) Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi
Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.
Emotion classification using single-channel scalp-EEG recording
(IEEE, 2016-10-18) Jalilifard, Amir; Pizzolato, Ednaldo Brigante; Islam, Md Kafiul
Several studies have found evidence for corticolimbic Theta electroencephalographic (EEG) oscillation in the neural processing of visual stimuli perceived as fear or threatening scene. Recent studies showed that neural oscillations' patterns in Theta, Alpha, Beta and Gamma sub-bands play a main role in brain's emotional processing. The main goal of this study is to classify two different emotional states by means of EEG data recorded through a single-electrode EEG headset. Nineteen young subjects participated in an EEG experiment while watching a video clip that evoked three emotional states: neutral, relaxation and scary. Following each video clip, participants were asked to report on their subjective affect by giving a score between 0 to 10. First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based denoising to remove artifacts. Afterward, the distribution of power in time-frequency space was obtained using short-time Fourier transform (STFT) and then, the mean value of energy was calculated for each EEG sub-band. Finally, 46 features, as the mean energy of frequency bands between 4 and 50 Hz, containing 689 instances — for each subject —were collected in order to classify the emotional states. Our experimental results show that EEG dynamics induced by horror and relaxing movies can be classified with average classification rate of 92% using support vector machine (SVM) classifier. We also compared the performance of SVM to K-nearest neighbors (K-NN). The results show that K-NN achieves a better classification rate by 94% accuracy. The findings of this work are expected to pave the way to a new horizon in neuroscience by proving the point that only single-channel EEG data carry enough information for emotion classification.
Probability Mapping Based Artifact Detection and Wavelet Denoising based Artifact Removal from Scalp EEG for BCI Applications
(IEEE, 2019-02-25) Islam, Md Kafiul; Rastegarnia, Amir
In EEG-based Brain-Computer Interface (BCI) applications, the EEG recording is often contaminated by different types of artifacts that can misinterpret the BCI output. Automatic detection and removal of such offending artifacts from EEG for online processing pose a great challenge. In this paper, we present a novel method that can map the artifact probability of an EEG epoch based on four statistical measures: entropy, kurtosis, skewness and Periodic Waveform Index PWI). Then a removal method is adopted based on stationary wavelet transform that can be applied to the epochs by setting a particular probability threshold from the user. This epoch by epoch preprocessing would allow the user to tune the threshold parameters after some initial training with the same EEG recordings and eventually can be applied to both offline and online processing. Experimental results with both simulated and real EEG data prove the efficacy of the method that it can reliably trace the artifactual epoch with reasonable accuracy and eventually reduces the artifacts from EEG with very little distortion to the signal of interest. Further testing with EEG datasets for BCI experiments also shows that artifact removal can significantly enhance the BCI performance in both motor-imagery (MI) and event related potential (ERP) based BCI applications.
Design and Implementation of an EOG-based Mouse Cursor Control for Application in Human-Computer Interaction
(IOP, 2020-03-01) Kabir, Ahsan-ul; Bin Shahin, Faisal; Islam, Md Kafiul
Human Computer Interaction (HCI) has turned into an emerging technology due to the advancement in the field artificial intelligence and biomedical engineering. Acquiring different bio-signals such as Electro-oculography (EOG), Electromyography (EMG) and Electroencephalography (EEG) to control external machine or computer is the essence of HCI technology. In this research, we attempt to extract the EOG signal from different ways of eye movements and process it for HCI application. By utilizing Arduino, EOG data can be transmitted to computer and those signal characteristics is analysed through MATLAB. We have designed and implemented hardware and interfaced it with software to control a computer mouse cursor only by eye movement. Certain classification module like Support Vector machine (SVM) and Multilayer Perceptron (MLP) are used to classify different EOG data generated from different eye movement. According to the eye position, cursor automatically moves in that specific direction and PyAutoGUI module is used for this task. Results after experimentations with different subjects to control mouse cursor in real-time show that the average classification accuracy can reach up to 93% across all directions.
Application of Machine Learning on ECG Signal Classification Using Morphological Features
(IEEE, 2020-06) Alim, Anika; Islam, Md. Kafiul
An electrocardiogram (ECG) is a simple test that is used to check one's heart's electrical activity. Sensors attached to the skin are used to detect the electrical signal produced by one's heart each time it beats. Many people around the world suffer from cardiovascular diseases. So it is important to detect arrhythmia/abnormal and normal ECG signal more accurately. In this paper, ECG signal is classified by support vector machine (SVM) and neural network. The research is conducted on the normal and arrhythmia ECG datasets obtained from the PhysioNet website. The raw ECG data are preprocessed using different filters and then the features are extracted based on morphological values of the waveform. Twelve features are extracted and these are used to train classifiers to classify normal and abnormal ECG data. For SVM classifier, the accuracy is around 87% while for artificial neural network, MATLAB's pattern recognition app is used where the classification accuracy found is around 90 % - 93%. The accuracy varies for different numbers of hidden neurons. Diverse ECG databases are used to prove the efficacy and robustness of the use of proposed morphological features and obtained results are compared with existing state-of-the-art research works. This work can be further developed in the future by incorporating deep learning for better performance and it can eventually help to detect cardiac diseases.
Use of spontaneous blinking for application in human authentication
(Elsevier, 2020-08) Jalilifard, Amir; Chen, Dehua; Mutasim, Aunnoy K.; Bashar, M. Raihanul; Tipu, Rayhan Sardar; Shawon, Ahsan-Ul Kabir; Sakib, Nazmus; Amin, M. Ashraful; Islam, Md. Kafiul
Contamination of electroencephalogram (EEG) signals due to natural blinking electrooculogram (EOG) signals is often removed to enhance the quality of EEG signals. This paper discusses the possibility of using solely involuntary blinking signals for human authentication. The EEG data of 46 subjects were recorded while the subject was looking at a sequence of different pictures. During the experiment, the subject was not focused on any kind of blinking task. Having the blink EOG signals separated from EEG, 25 features were extracted and the data were preprocessed in order to handle the corrupt or missing values. Since spontaneous and voluntary blinks have different characteristics in terms of kinematic variables and because the previous studies’ control setup may have altered the type of blink from spontaneous to voluntary, a series of statistical analysis was carried out in order to inspect the changes in the multivariate probability distribution of data compared to the previous studies. Statistical significance shows that it is very likely that the blink features of both voluntary and involuntary blink signal are generated by Gaussian probability density function, although different than voluntary blink, spontaneous blink is not well discriminated with Gaussian. Despite testing several models, none managed to classify the data using only the information of a single spontaneous blink. Thereby, we examined the possibility of learning the patterns of a series of blinks using Gated Recurrent Unit (GRU). Our results show that individuals can be distinguished with up to 98.7% accuracy using only a reasonably short sequence of involuntary blinking signals.
Classification of Emotions Induced by Horror and Relaxing Movies Using Single-Channel EEG Recordings
(Institute of Advanced Engineering and Science (IAES), 2020-08) Amir, Jalilifard; Amir, Rastegarnia; Ednaldo Birgante, Pizzolato; Md Kafiul, Islam
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm.Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (whenk= 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K >1 in K-NN, SVM has better average classification rate.
Performance Evaluation of 1kW Asynchronous and Synchronous Buck Converter-based Solar-powered Battery Charging System for Electric Vehicles
(IEEE, 2020-09-05) Das, Saurav; Haque, Md.Rezanul; Razzak, M. Abdur; Leon, Md Saiful Islam; Uddin, Mohammad Rejwan
This paper presents the design and evaluates the system performance of one-kilowatt capacity asynchronous and synchronous buck converter based solar-powered charging systems for battery-driven electric vehicles. The dc motor-operated three-wheeler rickshaw was taken for testing the systems, where a battery bank containing four series-connected sub-colloid storage type batteries of each with a capacity of 12V, 120Ah has been used. PSIM simulation software has been used to evaluate the performances of these two types of battery charging systems. Hardware prototypes of these two types of charging systems have also been made and an experimental testbed comprising a 48V battery bank of 100Ah capacity with a charging current of 6A was performed. The experimental results have also been evaluated and compared.
A Web-Based Hospital Automation System for Bangladesh
(International Journal of Pure and Applied Mathematics, 2020-09-21) Ahmed, S; Ahmad, M; Abedin, SZ; Rahman, A; Tahsin, KN; Khan, S; Islam, S
Medical record-keeping is well advanced in developing countries, but has just begun in developing countries like Bangladesh. The use of electronic medical systems can resolve many chronic inefficiencies and poor service common in developing countries. Implementation, legislation, and acceptance by practitioners are just some of the issues preventing the growth of medical record-keeping in Bangladesh. The outpatient department of hospitals in Bangladesh is usually insufficiently equipped and staff inadequately prepared to handle a large numbers of patients filing through per day. With these motivations, we investigated the current hand-written electronic medical record system in Bangladesh. We designed an automated medical record system to store, retrieve and process and share medical information in a computerized system.
Dual-Core Photonic Crystal Fiber-Based Plasmonic RI Sensor in the Visible to Near-IR Operating Band
(IEEE, 2020-10-04) Mahfuz, Mohammad Al; Hossain, Md. Anwar; Haque, Emranul; Hai, Nguyen Hoang; Namihira, Yoshinori; Ahmed, Feroz
In this paper, a dual-core photonic crystal fiber (DC-PCF) based surface plasmon resonance (SPR) bio-compatible sensor is proposed for various bio-organic molecules and biochemical analytes refractive index (RI) detection in the visible to near-infrared region (0.5 to 2 μm). Two hexagonal ring lattice with all circular air-holes are used to simplify the sensor structure. To make the practical applications feasible, plasmonic material and analyte sensing layer both are employed at the outer surface of the fiber. Noble plasmonic material gold (Au) having a thickness of 30 nm is used to excite the surface plasmons. A thin layer of titanium oxide (TiO 2 ) having a thickness of 5 nm is also considered as an adhesive layer between the Au and silica glass. The sensor response is investigated using the mode solver based finite element method (FEM). Numerical results indicate that the proposed sensor shows a maximum amplitude sensitivity (AS) of 6829 RIU -1 , amplitude resolution (AR) of 5 × 10 -6 RIU, maximum wavelength sensitivity (WS) of 28,000 nm/RIU, and wavelength resolution (WR) of 3.57 × 10 -6 RIU, using the amplitude and wavelength interrogation methods, respectively. Moreover, a maximum figure of merit (FOM) of 2800 RIU -1 is obtained, which is the highest among the reported PCF-SPR sensor. Owing to the promising sensitivity and simple structure, the proposed sensor can be potentially applicable for the detection of biochemical solutions and biological samples.
Effect of varying the row and column size of periodic arrays of plasmonic nanoparticles on the energy conversion efficiency of thin-film solar cells
(2020-10-07) Choudhury, Saniat Ahmed; Munir, Md. Shirazim; Nuzhat, Nawshin; Chowdhury, Mustafa Habib
The use of plasmonic nanostructures in enhancing the energy conversion efficiency of solar cells has been of great interest in recent times. While much of this interest has resulted in research for analyzing the metals that are most suitable for the plasmonic nanostructures, little attention has been given to optimizing the physical parameters of the nanostructures. The nanostructures that are of particular interest to this study are periodic nanoparticle arrays placed over thin-film amorphous silicon substrate. The extent to which the periodicity of the nanoparticle array affects the energy conversion efficiency of the solar cell has not been analyzed extensively. To this end, this paper investigates the periodic nature of the plasmonic metal nanoparticle array, and the relationship of the periodicity of the plasmonic nanostructured arrays to the optical and electrical enhancement obtained from coupling the nanoparticle arrays to thin-film amorphous silicon solar cells. It was found that increasing the number of rows and columns of the plasmonic nanoparticles in the array increases the observed optical and electrical signal enhancements from the thin-film solar cell. Additionally, it was also found that the optical and electrical enhancement of the solar cell depended significantly on the orientation of the nanoparticle array with respect to the axis of the polarization of the incident radiation.