Zaid Shuqfa
Zaid Shuqfa has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Abderrahmane Lakas
Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
2 shared publications Abdelkader Nasreddine Belkacem
Connected Autonomous Intelligent Systems Lab, Department of Computer and Network Engineering, College of IT (CIT), United Arab Emirates University (UAEU), Al Ain City 15551, the United Arab Emirates
1 shared publication- Abdelkader Nasreddine Belkacem
Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
1 shared publication - Balqis Albreiki
Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain, UAE
1 shared publication - Mohamed Adel Serhani
University of Sharjah
1 shared publication - Nazar Zaki
United Arab Emirates University
1 shared publication
Latest Publications
- Open Access
- 12 February 2024
- Published by Elsevier in Data in Brief
- Vol. 54, 110181
Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification
- 12 February 2024
- Published by Elsevier in Data in Brief
- Vol. 54, 110181
Open AccessIncreasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification
- Open Access
Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.
Open AccessDecoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.
- Open Access
- 29 November 2021
- Published by MDPI AG in Big Data and Cognitive Computing
- Vol. 5 (4) , 71
Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions
Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.- 29 November 2021
- Published by MDPI AG in Big Data and Cognitive Computing
- Vol. 5 (4) , 71
Open AccessCustomized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions
Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.