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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.
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.
A Machine Learning Approach for Multi-Level Anxiety Screening among University-Going Students using Wireless EEG Signals
(IEEE, 2025-06-23) Sakib, Nazmus; Islam, Md Kafiul; Faruk, Tasnuva
Anxiety is a widespread mental health condition affecting millions globally, often resulting in significant emotional and physical symptoms. Accurate detection of anxiety levels is essential to provide timely interventions and prevent severe complications. This study explores a machine learning-based approach for multilevel anxiety classification among young adults using EEG signals. The GAD-7 screening tool was used to assess and categorize participants into different anxiety severity groups. EEG data was then recorded, processed, and segmented into 1, 3, and 5 second segments to evaluate the impact of segment duration on classification accuracy. Four channel combinations were tested for comparisons in performance. Feature extraction included eleven time and frequency domain features. The Bagged Trees classifier was applied to classify anxiety levels based on these features. The findings of this work show the potential of EEG-based systems as non-invasive tools for anxiety screening that could support more precise mental health diagnostics.
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.
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.
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.
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.
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
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.
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.
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.
Distance Dependent Service Differentiation of the IEEE 802.11e EDCA on Single Access Point Based WLAN Systems
(Journal of the Bangladesh Electronics Society, 2020-10-19) Chowdhury, N. M. Shafiul Kabir; Hussain, Md. Shahriar; Sultana, Afroza; Ahmed, Farruk
The IEEE 802.11e Enhanced Distributed Channel Access (EDCA) protocol allows class based differentiated Quality of service (QoS) in a wireless local area network (WLAN). Different fixed values of two certain parameters; contention window (CW) and arbitration inter frame space number (AIFSN), ensure higher or lower priority among this traffic classes, administrating different QoS in terms of throughput, delay, jitter etc. Previous simulation study illustrated, superior throughput and delay performance achieved by the highest priority voice traffic, compared to whatever achieved by the lowest priority background traffic; according to the deliberate design of the IEEE 802.11e EDCA protocol. In this paper, we present our simulation study of the EDCA mechanism; augmented with the International Telecommunication Union (ITU) indoor propagation model, solidifying the outcome by ensuring an indoor or semi-indoor setup like todays real world WLAN system’s deployment scenarios. Simulation study shows that a node accessing highest priority traffic through an AP from a high distance at high data rate not only suffers performance drops itself but also severely bottlenecks the performance of other client nodes accessing traffics with comparatively lower priority, even if those nodes are at close proximity from the AP. However, the negative impact over other traffic accessing nodes are much lower but not fully negligible, when a client node through the AP, tries to access lower priority traffic from a large distance. Hence, the intended service differentiation over different traffic classes closely depends on whether all the client nodes are at a close proximity from the AP.
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.
Editorial: Recent advances in EEG (non-invasive) based BCI applications
(Frontiers, 2023-03-02) Rastegarnia, Amir; Islam, Md Kafiul
EEG-based Mouse Cursor Control using Motor Imagery Brain-Computer Interface
(IEEE, 2024-05-03) Roja, Saima Tasfia; Bin Rafique, Sayem; Rhaman, Md. Asikur; Sakib, Nazmus; Islam, Md Kafiul
Brain-computer interface (BCI) is a system that collects, analyzes, and transforms brain signals into commands. The brain experiences repetitive, oscillatory electrical changes caused by these activities that have a very low voltage of only a few microvolts (µV).The term electroencephalogram (EEG) refers to the non-invasive recording of this electrical activity from the scalp. The signals are then analyzed in a computer to identify the desired action after signal acquisition. Relevant features are gathered and translated into commands that operate an output device or carry out the command. The user is subsequently provided with feedback to confirm that the command has been carried out correctly This work focuses on the development and implementation of a Mouse Cursor Control system using Motor Imagery (MI) BCI, using data recorded with the Emotiv EPOC+ headset and processed using our algorithm in the MATLAB software. While similar works do exist, most tend to focus on one or more aspects of data processing such as classification. We acquired the data from subjects ourselves, and after processing the data using our algorithm, the system was implemented, and the cursor was moved. This makes our system a semi-online system, as opposed to offline systems. The only limitation of our system is that the system is implemented in semi-real time. Furthermore, accuracy was tested for different frequency bands and the highest accuracy of 93.60% was achieved using the offline dataset.
EEG-based Mouse Cursor Control using Motor Imagery-based Brain-Computer Interface
(2023-10-01) Rafique, Sayem Bin; Roja, Saima Tasfia; Rhaman, Md. Asikur
A brain-computer interface (BCI) framework uses computer algorithms to detect mental activity patterns and manipulate external devices. Most commonly used in imaging technologies is electroencephalography (EEG) because of its non-invasiveness. The evaluation method used in assessing the output of an EEG-based BCI system is classifying EEG signals for particular applications. In this study, we present a system of EEG-based mouse cursor control using a Motor Imagery-based Brain-Computer Interface (MI-BCI). The growth of technology and artificial intelligence inspired us to develop a system for physically impaired individuals as well as to work with electroencephalogram (EEG) signals. This signal is a noninvasive and low-cost method to extract brain signals from a subject. Our work also includes the EEG signal acquisition as well as advanced signal processing methods to utilize the MI-BCI-based brain activity. This work also includes the machine learning algorithm which carried out the system to do the successful cursor movement using binary classification. Furthermore, the successful mouse cursor movement added up the higher accuracy of 93.83% which is the result of the offline dataset.
EEG-Based Preference Classification for Neuromarketing Application
(Hindawi, 2023-03-01) Sourov, Injamamul Haque; Alvi Ahmed, Faiyaz; Opu, Md. Tawhid Islam; Mutasim, Aunnoy K.; Bashar, M. Raihanul; Sardar Tipu, Rayhan; Amin, Md. Ashraful; Islam, Md Kafiul
Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.
Effect of artifact removal on EEG based motor imagery BCI applications
(SPIE Digital Library, 2024-01-29) Islam, Md Kafiul; Sakib, Nazmus; Anjum, Maisha
Brain computer interface (BCI) is an emerging technology where the user can establish direct communication between the electrical device and himself without any physical exertion. The EEG signal is a noninvasive and low-cost method to extract brain signal from subject. The EEG signal contains different types of information including motor sensory information originating from the motor cortex region of the brain. Research and study have shown that motor cortex generates signals similar to the signals generated during deliberate limb movements. Therefore, motor imagery (MI) signals if extracted can be utilized to operate any electrical device establishing a BCI system. However, the EEG data can contain lots of artifacts. This degrades the signal quality and also cause false positive command to the connected device. Therefore, it is crucial to remove the artifacts from the EEG signal before classification. In this project, EEG data has been collected from 12 subjects who are instructed to perform MI activity. The EEG signal is then processed and an efficient artifact removal technique has been applied. The artifact removal method applies wavelet transform theorem and artifactual probability mapping method to detect artifactual epochs and eliminate it from the signal. Useful features are then extracted from the signal and artificial neural network (ANN) classifier is applied to it. The classification accuracy has been enhanced by 15-16% on average after removal of artifacts from the EEG recordings for MI-BCI experiments. Afterwards, performance evaluation such as finding signal to noise ratio has been done to evaluate the improvement in the signal after noise removal.
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.
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.