Tasnim Ferdous completed her Master's in Electrical and Computer Engineering from the Oregon State University, USA. Prior to this, she received her BSc. in EEE from AIUB, Bangladesh in 2012. After her masters, she worked as an Electrical Engineer in Electro Scientific Industries, Portland, OR, USA.
Once she returned back to Bangladesh, she joined Northern University Bangladesh as a faculty. After serving there a year, she joined Brac University.

Her current research interests are in Computational Neuroscience, Neuroinformatics and Digital Health, Biomedical Signal processing and Deep learning. Prior to this, she had research interest and hands-on experience in Analog/Mixed-signal Integrated circuit analysis, design and testing. 

Beside working, she loves to sketch and frame beautiful sights on paper. She also spends time reading on various topics, hiking within the woods and trying out different recipes.

Google Scholar
ORCID

1. Computational Neuroscience/ Brain Computer Interface(BCI)
   Neuroinformatics and Digital health
   Biomedical Signal processing.
   Sensor electronics, Machine Learning and Deep learning applications

1. Brain Computer Interface based gamified neurofeedback (ONGOING)
   This study integrates Brain-Computer Interface (BCI) technology to enhance attention, memory, and manage fatigue-induced drowsiness. It combines Beta-to-(Alpha+Theta) Ratio and Frontal Midline Theta (FM-theta) neurofeedback with eye tracking to stimulate brain activity associated with cognitive performance. A gamified platform provides real-time EEG feedback, while advanced neural networks classify mental states for effective cognitive enhancement.
   -Team members: Ms. Tasnim Ferdous, Dr. Aniqua Nusrat Zereen, Shararah Kibria, Zonaed Ahmed

Machine and Deep learning on PPG based automated Sleep stage classification   (ONGOING)
This project focuses on creating a deep learning model that uses PPG signals to classify sleep stages accurately. The model will be trained on data from both healthy people and those with mild sleep problems, aiming to detect abnormal Critical Arousal Periods (CAP) linked to sleep disorders. It will use techniques like PCA for feature reduction and attention-based CNNs, along with Explainable AI (XAI), to highlight the most important features. The model will also be adapted for smartwatches, allowing real-time, home-based sleep tracking for both healthy individuals and those with sleep issues.
-Team members: Ms. Tasnim Ferdous, Dr. Aniqua Nusrat Zereen, Naveed Mahmood

AI-Based Early Detection of Parkinson’s Disease from Facial Emotion and Motor Symptoms 
An AI-based approach using computer vision for the early detection of Parkinson’s disease by analyzing facial emotions and motor symptoms. Deep learning models will process subtle changes in facial expressions and movement patterns from video data to identify early biomarkers. The study aims to develop an efficient, non-invasive diagnostic tool for timely intervention and improved patient outcomes.

EMG-Based Adaptive Mobile Interaction System for Visually Impaired Users: Gesture Recognition and AI-Driven Control (ONGOING)
An EMG-based adaptive mobile interaction system for visually impaired users to control devices using muscle gestures. Wearable EMG sensors detect gestures, providing haptic and audio feedback. An AI-driven model enhances real-time intent recognition. The project includes a wearable prototype, gesture recognition software, and user trials. It aims to improve accessibility, reduce reliance on voice assistants, and enable independent device control. Challenges include EMG signal noise and user variability, while opportunities arise from the growing demand for assistive tech.
-Team members: Ms. Tasnim Ferdous, MD. Rezaur Rahman Bhuyan, Dr. Khalilur Rahman.