Overview
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.
In computational neuroscience, he works on the development of multidimensional connectivity and decoding analysis methods to study information coding and transfer across the brain. His cognitive neuroscience interests include reearch into the neural bases of visual perception, attention and the multiple-demand system. He also develops methods to quantify and localise brain areas involved in epilepsy.
Publications
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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
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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
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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
Grants
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Characterising brain networks of intelligence through information tracking
(2023–2029) ARC Discovery Early Career Researcher Award
Available Projects
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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.
Publications
Journal Article
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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
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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
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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
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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
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#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
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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
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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
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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
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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
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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
Grants (Administered at UQ)
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Characterising brain networks of intelligence through information tracking
(2023–2029) ARC Discovery Early Career Researcher Award
Possible Research Projects
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.
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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.
