Interests and Expertise

Highlights of our research

Ian Daly’s research centres on BCIs and neurotechnologies to understand and enhance human motor and cognitive function. Ian research includes designing BCIs for communication, affective and passive interfaces that sense users’ emotional or cognitive states; advancing signal processing and machine learning for EEG and other neural data to make BCIs more accurate and adaptive; investigating how neuromodulation techniques can promote motor learning and drive recovery in neurorehabilitation. Ian’s work integrates closed-loop stimulation, motor training, and neural decoding to study the mechanisms of plasticity and to create personalised interventions that restore or extend motor and cognitive abilities.

Junhua Li is engaged in research at the intersection of artificial intelligence and healthcare, which can be broadly categorised into three primary topics:

1. Development of Machine Learning Algorithms: design advanced deep learning models and tensor decomposition for a range of applications. Representative works include deep subdomain adaptation and the MSC-Transformer framework.
2. Mental State and Health Monitoring Systems: assess and monitor cognitive and psychological states, mental workload and mental fatigue detection.
3. Brain Disease Understanding and Detection: analyse and classify neuroimaging and related data to better understand and detect neurological disorders. Example contributions include the study of connectivity alterations associated with schizophrenia and the detection of schizophrenia using machine learning techniques.

Muhammad Tariq Sadiq's research focuses on EEG-based biomedical signal processing, brain–computer interfaces, and neurorobotics. He develops novel time–frequency analysis methods, feature engineering, and explainable AI frameworks for reliable detection of neurological disorders (e.g., dementia, depression, epilepsy, Parkinson’s disease, alcoholism) and for motor intention decoding in BCIs. He is particularly interested in translational neurotechnology for rehabilitation, mental health, and human–robot interaction. Some recent representative publications include:

• Siuly, S., Khare, S.K., Kabir, E., Sadiq, M.T., Wang, H. (2024). An Efficient Parkinson's Disease Detection Framework: Leveraging Time–Frequency Representation and AlexNet Convolutional Neural Network. Computers in Biology and Medicine, 174, 108462. 
• Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z., Rehman, N., Ding, W., Xiao, G. (2022). Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition. IEEE Transactions on Emerging Topics in Computational Intelligence. 
• Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z., Siuly, S., Ding, W., et al. (2021). Toward the Development of Versatile Brain–Computer Interfaces. IEEE Transactions on Artificial Intelligence, 2(4), 314–328.