N
Nasreen Badruddin
Researcher at Universiti Teknologi Petronas
Publications - 86
Citations - 1495
Nasreen Badruddin is an academic researcher from Universiti Teknologi Petronas. The author has contributed to research in topics: Artifact (error) & Equal-cost multi-path routing. The author has an hindex of 16, co-authored 79 publications receiving 1083 citations. Previous affiliations of Nasreen Badruddin include University of Melbourne & Petronas.
Papers
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Journal ArticleDOI
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.
Hafeez Ullah Amin,Aamir Saeed Malik,Rana Fayyaz Ahmad,Nasreen Badruddin,Nidal Kamel,Muhammad Hussain,Weng-Tink Chooi +6 more
TL;DR: It is demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.
Journal ArticleDOI
A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.
TL;DR: The proposed method to detect drowsiness in drivers which integrates features of electrocardiography and electroencephalography to improve detection performance demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone.
Journal ArticleDOI
Mental stress assessment using simultaneous measurement of EEG and fNIRS
Fares Al-Shargie,Masashi Kiguchi,Nasreen Badruddin,Sarat C. Dass,Ahmad Fadzil Mohammad Hani,Tong Boon Tang +5 more
TL;DR: This study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively.
Journal ArticleDOI
Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach
TL;DR: The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index and developed a discriminant analysis method based on multiclass support vector machine with error-correcting output code (ECOC).
Book ChapterDOI
Mental Stress Quantification Using EEG Signals
TL;DR: The experimental results showed that there were significant differences between the rest state and under stress at three levels of arithmetic task levels with p-values of 0.03, 0.042 and 0.05, which confirms the feasibility of EEG signals in detecting mental stress levels.