https://ar.iub.edu.bd/handle/11348/258 2026-04-09T15:39:49Z 2026-04-09T15:39:49Z Sakib, Nazmus Islam, Md Kafiul Faruk, Tasnuva https://ar.iub.edu.bd/handle/11348/1041 2026-02-26T06:35:57Z 2025-06-23T00:00:00Z

A Machine Learning Approach for Multi-Level Anxiety Screening among University-Going Students using Wireless EEG Signals 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. Conference Paper

2025-06-23T00:00:00Z Rafique, Sayem Bin Roja, Saima Tasfia Rhaman, Md. Asikur https://ar.iub.edu.bd/handle/11348/1040 2026-02-26T06:36:12Z 2023-10-01T00:00:00Z

EEG-based Mouse Cursor Control using Motor Imagery-based Brain-Computer Interface 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.

2023-10-01T00:00:00Z Roja, Saima Tasfia Bin Rafique, Sayem Rhaman, Md. Asikur Sakib, Nazmus Islam, Md Kafiul https://ar.iub.edu.bd/handle/11348/949 2024-06-18T18:45:10Z 2024-05-03T00:00:00Z

EEG-based Mouse Cursor Control using Motor Imagery Brain-Computer Interface 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.

2024-05-03T00:00:00Z Islam, Md Kafiul Sakib, Nazmus Anjum, Maisha https://ar.iub.edu.bd/handle/11348/941 2026-02-26T06:35:28Z 2024-01-29T00:00:00Z

Effect of artifact removal on EEG based motor imagery BCI applications 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.

2024-01-29T00:00:00Z