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Item 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, TasnuvaAnxiety 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.Item EEG-based Mouse Cursor Control using Motor Imagery-based Brain-Computer Interface(2023-10-01) Rafique, Sayem Bin; Roja, Saima Tasfia; Rhaman, Md. AsikurA 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.Item 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 KafiulBrain-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.Item Effect of artifact removal on EEG based motor imagery BCI applications(SPIE Digital Library, 2024-01-29) Islam, Md Kafiul; Sakib, Nazmus; Anjum, MaishaBrain 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.Item Editorial: Recent advances in EEG (non-invasive) based BCI applications(Frontiers, 2023-03-02) Rastegarnia, Amir; Islam, Md KafiulItem 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 KafiulNeuromarketing 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.Item Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset(Hindawi, 2023-05-31) Islam, Md Kafiul; Faruk, Tasnuva; Sakib, NazmusDepression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.Item Intelligent fuzzy system for automatic artifact detection and removal from EEG signals(Elsevier, 2022-10-05) Agounad, Said; Hamou, Soukaina; Tarahi, Ousama; Moufassih, Mustapha; Islam, Md KafiulThe EEG signals were used in many medical and technological applications such as diagnosis of diseases, rehabilitation of disabled peoples, preventive healthcare, BCI (brain computer interface) systems. EEG signal is prone to the physiological and non-physiological artifacts which severely affect them and lead to its misinterpretation. An automatic method and/or algorithm; for handling EEG artifacts; is proposed. The proposed method is based on three statistical parameters (entropy, kurtosis and skewness), fuzzy inference system (FIS) and stationary wavelet transform (SWT). Each incoming EEG epoch is described using these three statistical parameters. Based on the extracted statistical parameters, the designed FIS decides if an epoch is artifactual or not. Then SWT is used to decompose the EEG epoch into detail and approximation coefficients. To reduce the effect of artifact removal, we propose to use other fuzzy inference systems, which allow to select the contaminated wavelet coefficients. The universal thresholding method is then applied to the corrupted coefficients. Finally, the inverse SWT applies to the thresholded and non-corrupted coefficients to restore the cleaned EEG signal. The performance of the proposed method in terms of amount of artifact removal and signal distortion is evaluated in three scenarios: fully simulated, semi-simulated, and real artifactual EEG data. The comparison of our method with some existing state-of-the-art methods shows the superiority of our method over others in terms of performance and computational time.Item Application of Machine Learning on ECG Signal Classification Using Morphological Features(IEEE, 2020-06) Alim, Anika; Islam, Md. KafiulAn 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.Item RFID in Healthcare Systems:(2020-10-19) Sultana, Afroza; Sobhan, AbdusEveryone has heard about tragic wrong-site, wrong-patient and wrong-procedure surgeries. Using an RFID tag attached to a patient, a physician can now verify the correct patient, procedure and site − prior to the start of any invasive procedure.. The health care community is now seeing tremendous benefits of RFID technology that can contribute to the healthcare industry with Wi-Fi and voice over IP (VoIP), creating a single information system that can track patients and hospital assets, improve patient safety, play a role in running clinical trials of drugs, manage critical care of assets and hospital equipment. It can reduce counterfeiting of pharmaceutical products, medical errors, and cut costs improving efficiency. In this paper, authors discuss about the application, benefits and challenges of Radio Frequency Identification (RFID) in healthcare.