Having done a Newton Fellowship at MRC Cognition and Brain Sciences Unit, The University of Cambridge, Dr Hamid Karimi-Rouzbahani is now an ARC DECRA fellow at The University of Queensland.
His interests are at the intersection of Computational, Cognitive and Clinical Neuroscience and combine neural signal processing (e.g., EEG, MEG and fMRI), machine learning (e.g., deep neural networks) and mathematical modelling.
His computational work involve the development of multidimensional connectivity and decoding analysis methods to study information coding and transfer across the brain. His cognitive interests include research into the neural bases of visual perception, attention and the multiple-demand system. His clinical work develops methods to quantify and localise brain areas involved in epilepsy.
Journal Article: Generalisability of epileptiform patterns across time and patients
Karimi-Rouzbahani, Hamid and McGonigal, Aileen (2024). Generalisability of epileptiform patterns across time and patients. Scientific Reports, 14 (1) 6293, 1-14. doi: 10.1038/s41598-024-56990-7
Journal Article: Evidence for Multiscale Multiplexed Representation of Visual Features in EEG
Karimi-Rouzbahani, Hamid (2024). Evidence for Multiscale Multiplexed Representation of Visual Features in EEG. Neural Computation, 36 (3), 412-436. doi: 10.1162/neco_a_01649
Journal Article: Correction: Neural signatures of vigilance decrements predict behavioural errors before they occur
Karimi-Rouzbahani, Hamid, Woolgar, Alexandra and Rich, Anina N (2023). Correction: Neural signatures of vigilance decrements predict behavioural errors before they occur. eLife, 12. doi: 10.7554/elife.91529
Characterising brain networks of intelligence through information tracking
(2023–2029) ARC Discovery Early Career Researcher Award
Developing novel information decoding and tracking methods to study brain and cognition
The Brain is one of the most complicated information processing systems known. However, we have not yet fully discovered how the brain processes information and solves complicated cognitive problems. This project is aimed at enhancing state-of-the-art methodologies in neural data analysis. While great progress has been made in the past decades on developing methods for neural data analysis, the development of knowledge now allows us to develop methods which can provide unprecedented insights into the brain. This project works on two aspects of neural information processing including how neural activations reflect meaningful information and how those activations transfer information from one area of the brain to another indifferent tasks.
This project involves programming in different programming languages including PYTHON and MATLAB and analysing different modalities of neural data including electroencephalography (EEG), magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), neurophysiology data and calcium imaging. These datasets will be collected either in the lab by the PhD student and/or obtained from publicly available sources. The project also uses stimulation devices such as Transcranial Magnetic Stimulation (TMS) to evaluate causal role of interference on human cognition.
Generalisability of epileptiform patterns across time and patients
Karimi-Rouzbahani, Hamid and McGonigal, Aileen (2024). Generalisability of epileptiform patterns across time and patients. Scientific Reports, 14 (1) 6293, 1-14. doi: 10.1038/s41598-024-56990-7
Evidence for Multiscale Multiplexed Representation of Visual Features in EEG
Karimi-Rouzbahani, Hamid (2024). Evidence for Multiscale Multiplexed Representation of Visual Features in EEG. Neural Computation, 36 (3), 412-436. doi: 10.1162/neco_a_01649
Correction: Neural signatures of vigilance decrements predict behavioural errors before they occur
Karimi-Rouzbahani, Hamid, Woolgar, Alexandra and Rich, Anina N (2023). Correction: Neural signatures of vigilance decrements predict behavioural errors before they occur. eLife, 12. doi: 10.7554/elife.91529
Mokari-Mahallati, Masoumeh, Ebrahimpour, Reza, Bagheri, Nasour and Karimi-Rouzbahani, Hamid (2023). Deeper neural network models better reflect how humans cope with contrast variation in object recognition. Neuroscience Research, 192, 48-55. doi: 10.1016/j.neures.2023.01.007
Examining the generalizability of research findings from archival data
Delios, Andrew, Clemente, Elena Giulia, Wu, Tao, Tan, Hongbin, Wang, Yong, Gordon, Michael, Viganola, Domenico, Chen, Zhaowei, Dreber, Anna, Johannesson, Magnus, Pfeiffer, Thomas, Uhlmann, Eric Luis, Al-Aziz, Ahmad M. Abd, Abraham, Ajay T., Trojan, Jais, Adamkovic, Matus, Agadullina, Elena, Ahn, Jungsoo, Akinci, Cinla, Akkas, Handan, Albrecht, David, Alzahawi, Shilaan, Amaral-Baptista, Marcio, Anand, Rahul, Ang, Kevin Francis U., Anseel, Frederik, Aruta, John Jamir Benzon R., Ashraf, Mujeeba, Baker, Bradley J. ... Zultan, Ro'i (2022). Examining the generalizability of research findings from archival data. Proceedings of the National Academy of Sciences of the United States of America, 119 (30) e2120377119, 1-9. doi: 10.1073/pnas.2120377119
Caveats and nuances of model-based and model-free representational connectivity analysis
Karimi-Rouzbahani, Hamid, Woolgar, Alexandra, Henson, Richard and Nili, Hamed (2022). Caveats and nuances of model-based and model-free representational connectivity analysis. Frontiers in Neuroscience, 16 755988. doi: 10.3389/fnins.2022.755988
Karimi-Rouzbahani, Hamid and Woolgar, Alexandra (2022). When the whole is less than the sum of its parts: maximum object category information and behavioral prediction in multiscale activation patterns. Frontiers in Neuroscience, 16 825746. doi: 10.3389/fnins.2022.825746
#EEGManyLabs: Investigating the replicability of influential EEG experiments
Pavlov, Yuri G., Adamian, Nika, Appelhoff, Stefan, Arvaneh, Mahnaz, Benwell, Christopher S. Y., Beste, Christian, Bland, Amy R., Bradford, Daniel E., Bublatzky, Florian, Busch, Niko A., Clayson, Peter E., Cruse, Damian, Czeszumski, Artur, Dreber, Anna, Dumas, Guillaume, Ehinger, Benedikt, Ganis, Giorgio, He, Xun, Hinojosa, José A., Huber-Huber, Christoph, Inzlicht, Michael, Jack, Bradley N., Johannesson, Magnus, Jones, Rhiannon, Kalenkovich, Evgenii, Kaltwasser, Laura, Karimi-Rouzbahani, Hamid, Keil, Andreas, König, Peter ... Mushtaq, Faisal (2021). #EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex, 144, 213-229. doi: 10.1016/j.cortex.2021.03.013
Karimi-Rouzbahani, Hamid, Shahmohammadi, Mozhgan, Vahab, Ehsan, Setayeshi, Saeed and Carlson, Thomas (2021). Temporal variabilities provide additional category-related information in object category decoding: a systematic comparison of informative eeg features. Neural Computation, 33 (11), 3027-3072. doi: 10.1162/neco_a_01436
Dissociable contribution of extrastriate responses to representational enhancement of gaze targets
Merrikhi, Yaser, Shams-Ahmar, Mohammad, Karimi-Rouzbahani, Hamid, Clark, Kelsey, Ebrahimpour, Reza and Noudoost, Behrad (2021). Dissociable contribution of extrastriate responses to representational enhancement of gaze targets. Journal of Cognitive Neuroscience, 33 (10), 2167-2180. doi: 10.1162/jocn_a_01750
Perceptual difficulty modulates the direction of information flow in familiar face recognition
Karimi-Rouzbahani, Hamid, Ramezani, Farzad, Woolgar, Alexandra, Rich, Anina and Ghodrati, Masoud (2021). Perceptual difficulty modulates the direction of information flow in familiar face recognition. NeuroImage, 233 117896. doi: 10.1016/j.neuroimage.2021.117896
Neural signatures of vigilance decrements predict behavioural errors before they occur
Karimi-Rouzbahani, Hamid, Woolgar, Alexandra and Rich, Anina N. (2021). Neural signatures of vigilance decrements predict behavioural errors before they occur. eLife, 10 e60563. doi: 10.7554/ELIFE.60563
Karimi-Rouzbahani, Hamid, Vahab, Ehsan, Ebrahimpour, Reza and Menhaj, Mohammad Bagher (2019). Spatiotemporal analysis of category and target-related information processing in the brain during object detection. Behavioural Brain Research, 362, 224-239. doi: 10.1016/j.bbr.2019.01.025
Karimi-Rouzbahani, Hamid (2018). Three-stage processing of category and variation information by entangled interactive mechanisms of peri-occipital and peri-frontal cortices. Scientific Reports, 8 (1) 12213, 12213. doi: 10.1038/s41598-018-30601-8
Karimi-Rouzbahani, Hamid, Bagheri, Nasour and Ebrahimpour, Reza (2017). Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models. Scientific Reports, 7 (1) 14402. doi: 10.1038/s41598-017-13756-8
Karimi-Rouzbahani, Hamid, Bagheri, Nasour and Ebrahimpour, Reza (2017). Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition. Neuroscience, 349, 48-63. doi: 10.1016/j.neuroscience.2017.02.050
Karimi-Rouzbahani, Hamid, Bagheri, Nasour and Ebrahimpour, Reza (2017). Average activity, but not variability, is the dominant factor in the representation of object categories in the brain. Neuroscience, 346, 14-28. doi: 10.1016/j.neuroscience.2017.01.002
Diagnosis of Parkinson's disease in human using voice signals
Rouzbahani, Hamid Karimi and Daliri, Mohammad Reza (2011). Diagnosis of Parkinson's disease in human using voice signals. Basic and Clinical Neuroscience, 2 (3), 12-20.
Characterising brain networks of intelligence through information tracking
(2023–2029) ARC Discovery Early Career Researcher Award
Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.
Developing novel information decoding and tracking methods to study brain and cognition
The Brain is one of the most complicated information processing systems known. However, we have not yet fully discovered how the brain processes information and solves complicated cognitive problems. This project is aimed at enhancing state-of-the-art methodologies in neural data analysis. While great progress has been made in the past decades on developing methods for neural data analysis, the development of knowledge now allows us to develop methods which can provide unprecedented insights into the brain. This project works on two aspects of neural information processing including how neural activations reflect meaningful information and how those activations transfer information from one area of the brain to another indifferent tasks.
This project involves programming in different programming languages including PYTHON and MATLAB and analysing different modalities of neural data including electroencephalography (EEG), magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), neurophysiology data and calcium imaging. These datasets will be collected either in the lab by the PhD student and/or obtained from publicly available sources. The project also uses stimulation devices such as Transcranial Magnetic Stimulation (TMS) to evaluate causal role of interference on human cognition.