School of Mechanical & Manufacturing Engineering

Next-gen Human Machine Technologies

In the field of Human Machine interfaces (HCIs), this group works on developing algorithms for biomedical signal decoding and feedback systems. By integrating advanced tools like artificial intelligence and machine learning, this group aims to create adaptive solutions to improve communication, rehabilitation for diverse needs. The group also explores how brain activity relates to cognitive functions and individual differences, striving to uncover the mechanisms behind mental processes. By combining insights from biomedical engineering, neuroscience, engineering, and technology, our goal is to develop innovative solutions that address real-world challenges and improve lives.
  • Biomedical Signal Processing.
  • Behavioural Neuroscience.
  • Assistive and Rehabilitation Engineering.

Our Team

Meet Our Research Team


Dr Muhammad Nabeel Anwar
Group Lead – PhD – Biomedical Engineering And Bioelectronics

 

 

 

 


Dr Danyal Mahmood
PhD – EEG Signal processing and Analysis

Next-gen Human Machine Technologies

1.     Afzal, U., Saeed, Q., Anwar, M. N., Pervaiz, S., Shahid, M., Javed, R., ... & Lee, S. W. (2024, August). Comparison of Health Parameters in Postpartum Diastasis Recti: A Randomized Control Trial of SEMG Biofeedback-Assisted Core Strengthening Exercises with Kinesiotaping vs. Non-Assisted Exercises. In Healthcare (Vol. 12, No. 16, p. 1567). MDPI.

2.     Afzal, N., Ramzan, B., Fatima, N., Khalid, M., Waris, A., Gilani, S. O., & Anwar, M. N. (2023, October). Plantar Pressure Response for Classification of Parkinson’s Disease patients with Gait Abnormalities using Machine Learning. In 2023 3rd International Conference on Digital Futures and Transformative Technologies (ICoDT2) (pp. 1-6). IEEE.

3.     Ahmad, H., Gori, M., Tonelli, A., Anwar, M. N., & Sandini, G. (2019, April). Visual Size Perception and Haptic Calibration After Late Emergence From Blindness. In PERCEPTION (Vol. 48, pp. 106-107). 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND: SAGE PUBLICATIONS LTD.

4.     Hadi, Z., Shakeel, A., Ahmad, H., Anwar, M. N., & Navid, M. S. (2019). The effect of single-task training on learning transfer to a novel bimanual task. bioRxiv, 858217.

5.     Shakeel, A., Ahmad, H., Navid, M. S., Mahroo, A., & Anwar, M. N. (2017, February). Performance feedback assists practice driven plasticity. In 2017 13th IASTED International Conference on Biomedical Engineering (BioMed) (pp. 1-4). IEEE.

6.     Akhtar, H., Bukhari, F., Nazir, M., Anwar, M. N., & Shahzad, A. (2016). Therapeutic efficacy of neurostimulation for depression: techniques, current modalities, and future challenges. Neuroscience bulletin, 32, 115-126.

7.     Anwar, M. N., Navid, M. S., Khan, M., & Kitajo, K. (2015). A possible correlation between performance IQ, visuomotor adaptation ability and mu suppression. Brain research, 1603, 84-93.

8.     Mahmood, D., Nisar, H., Yap, V. V., & Tsai, C. Y. (2022). The effect of music listening on EEG functional connectivity of brain: A short-duration and long-duration study. Mathematics, 10(3), 349.

9.     Mahmood, D., Nisar, H., & Voon, Y. V. (2021, December). Removal of physiological artifacts from electroencephalogram signals: a review and case study. In 2021 IEEE 9th Conference on Systems, Process and Control (ICSPC 2021) (pp. 141-146). IEEE.

10.  Mahmood, D., Nisar, H., & Tsai, C. Y. (2024). Exploring the efficacy of neurofeedback training in modulating alpha-frequency band and its effects on functional connectivity and band power. Expert systems with applications, 254, 124415.

11.  Mahmood, D., Nisar, H., Nawaz, R., Yap, V. V., & Tsai, C. Y. (2024). Attention-related power and functional connectivity modulation associated with long-term alpha neurofeedback training. Biomedical Signal Processing and Control, 87, 105431.

12.  Mahmood, D., Leong, H. J., & Nisar, H. (2022, September). Effectiveness of online ocular artifact removal from electroencephalogram signal during neurofeedback training. In 2022 International Conference on Emerging Trends in Smart Technologies (ICETST) (pp. 1-6). IEEE.

13.  Ong, C. W., Nisar, H., & Mahmood, D. (2025). Anxiety brain topographic maps and its classification using quantitative electroencephalography. In Intelligent Computing Techniques in Biomedical Imaging (pp. 217-238). Academic Press.

14.  Nisar, H., Ong, Y. N., & Mahmood, D. (2024, September). Analyzing the Effect of Relaxing Natural Sounds on the Human Brain During Alpha Up-Regulation Neurofeedback Training. In 2024 IEEE 8th International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 1-5). IEEE.

15.  Mahmood, D., Nisar, H., & Ali, S. A. (2024, June). Congruency Effect During Attention Network Task for Electroencephalogram-Based Alpha Neurofeedback Training. In 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS) (pp. 29-34). IEEE.
 
  1. Analysis of variations in brain states and impact of Stimulation during behavioral task
  2. AI for Data Sciences and Analytics – ICESCO
  3. Disability Inclusion – United Nations-ODC
1.     Social Media Analytics for Mental Health Assessment

2.     EEG-Based Biomarkers for Autism Spectrum Disorder Identification

3.     AI-Driven Early Detection of Neurofeedback Training Response Using EEG

4.     Enhancing EEG Signal Quality: Real-Time Artifact Removal Framework

5.     Multimodal Brain Signal Processing: EEG and BOLD Data Fusion

6.     Adaptive Posture Correction: A Wearable Smart Brace with Mobile Integration

7.     Sleep and Mobility in Amputees: A Wearable Data Approach to Rehabilitation and Fall Prevention