Publications by authors named "Nasreen Badruddin"

Introduction: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.

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Distributed video coding (DVC) is based on distributed source coding (DSC) concepts in which video statistics are used partially or completely at the decoder rather than the encoder. The rate-distortion (RD) performance of distributed video codecs substantially lags the conventional predictive video coding. Several techniques and methods are employed in DVC to overcome this performance gap and achieve high coding efficiency while maintaining low encoder computational complexity.

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  • Drowsiness is a significant factor contributing to road accidents, leading to interest in using EEG signals for detection.
  • This study explores extracting useful features from EEG ocular artifacts, typically discarded as noise, to differentiate between alert and drowsy states.
  • By applying the BLINKER algorithm to a dataset from 12 participants and optimizing various machine learning models, the research achieved a peak accuracy of 91.10% in classifying drowsiness based on these features.
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  • Mental stress significantly contributes to various health issues like heart disease and depression, necessitating effective stress quantification for intervention.
  • A study explored using electroencephalography (EEG) to differentiate stress levels during mental arithmetic tasks of varying difficulty, revealing notable EEG response differences with significant statistical outcomes.
  • An advanced method combining multiclass support vector machine and error-correcting output code achieved a high classification accuracy of 94.79%, highlighting the right prefrontal cortex's reduced alpha rhythm power as a key indicator of mental stress.
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Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study.

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Purpose: To localize sensorimotor cortical activation in 10 patients with frontoparietal tumors using quantitative magnetoencephalography (MEG) with noise-normalized approaches.

Material And Methods: Somatosensory evoked magnetic fields (SEFs) were elicited in 10 patients with somatosensory tumors and in 10 control participants using electrical stimulation of the median nerve via the right and left wrists. We localized the N20m component of the SEFs using dynamic statistical parametric mapping (dSPM) and standardized low-resolution brain electromagnetic tomography (sLORETA) combined with 3D magnetic resonance imaging (MRI).

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  • Researchers found that mental stress can lead to serious health problems like heart attacks and depression.
  • They proposed a new way to measure stress using two methods: EEG (which records brain waves) and fNIRS (which looks at blood flow in the brain).
  • Their tests showed that combining these methods gives better results in figuring out if someone is stressed compared to using just one method.
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Visual and mental fatigues induced by active shutter stereoscopic 3D (S3D) display have been reported using event-related brain potentials (ERP). An important question, that is whether such effects (visual & mental fatigues) can be found in passive polarized S3D display, is answered here. Sixty-eight healthy participants are divided into 2D and S3D groups and subjected to an oddball paradigm after being exposed to S3D videos with passive polarized display or 2D display.

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3D movies are attracting the viewers as they can see the objects flying out of the screen. However, many viewers have reported various problems which are usually faced after watching 3D movies. These problems include visual fatigue, eye strain, headaches, dizziness, blurred vision or collectively may be termed as visually induced motion sickness (VIMS).

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This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose.

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  • 3D stereoscopy technology offers immersive entertainment but isn't fully developed, leading to potential viewer discomfort.
  • Subjective data collected during a study indicated that viewers experienced higher levels of Visually Induced Motion Sickness (VIMS) when watching movies in 3D compared to 2D.
  • Objective measurements, including heart rate variability, did not show significant changes over the course of the experiment, suggesting the physiological response may not align with reported discomfort levels.
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