School of Computing

Publications by Dr Srivas Chennu

Also view these in the Kent Academic Repository

Article
Izziden, A. and Chennu, S. (2018). A Neuroscience Study on the Implicit Subconscious Perceptions of Fairness and Islamic Law in Muslims Using the EEG N400 Event Related Potential. Journal of Cognition and Neuroethics 5:21-50.
Thibaut, A. et al. (2018). Theta network centrality correlates with tDCS response in disorders of consciousness. Brain Stimulation [Online]. Available at: https://doi.org/10.1016/j.brs.2018.09.002.
Bareham, C. et al. (2018). Longitudinal Bedside Assessments of Brain Networks in Disorders of Consciousness: Case Reports from the Field. Longitudinal Bedside Assessments of Brain Networks in Disorders of Consciousness: Case Reports from the Field [Online]. Available at: https://www.frontiersin.org/articles/10.3389/fneur.2018.00676/full.
Shirazibeheshti, A. et al. (2018). Placing Meta-stable States of Consciousness within the Predictive Coding Hierarchy: the Deceleration of the Accelerated Prediction Error. Consciousness and Cognition [Online] 63:123-142. Available at: https://doi.org/10.1016/j.concog.2018.06.010.
Kuttikat, A. et al. (2017). Altered Neurocognitive Processing of Tactile Stimuli in Patients with Complex Regional Pain Syndrome (CRPS). The Journal of Pain [Online] 19:395-409. Available at: http://dx.doi.org/10.1016/j.jpain.2017.11.008.
Chennu, S. et al. (2017). Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain [Online] 140:2120-2132. Available at: http://dx.doi.org/10.1093/brain/awx163.
Chennu, S. et al. (2016). Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness. PLoS Computational Biology [Online] 12:1-17. Available at: http://dx.doi.org/10.1371%2Fjournal.pcbi.1004669.
Gibson, R. et al. (2016). Somatosensory attention identifies both overt and covert awareness in disorders of consciousness. Annals of Neurology [Online] 80:412-423. Available at: http://dx.doi.org/10.1002/ana.24726.
Kuttikat, A. et al. (2016). Neurocognitive and Neuroplastic Mechanisms of Novel Clinical Signs in CRPS. Frontiers in Human Neuroscience [Online] 10:1-13. Available at: http://dx.doi.org/10.3389/fnhum.2016.00016.
Panda, R. et al. (2016). Temporal dynamics of the default mode network characterise meditation induced alterations in consciousness. Frontiers in Human Neuroscience [Online] 10:1-12. Available at: http://dx.doi.org/10.3389/fnhum.2016.00372.
Naccache, L. et al. (2016). Reply: Replicability and impact of statistics in the detection of neural responses of consciousness. Brain [Online]:1-3. Available at: http://dx.doi.org/10.1093/brain/aww060.
Chennu, S. et al. (2016). Silent Expectations: Dynamic Causal Modeling of Cortical Prediction and Attention to Sounds That Weren't. Journal of Neuroscience [Online] 36:8305-8316. Available at: http://dx.doi.org/10.1523/JNEUROSCI.1125-16.2016.
Beukema, S. et al. (2016). A hierarchy of event-related potential markers of auditory processing in disorders of consciousness. NeuroImage: Clinical [Online] 12:359-371. Available at: http://doi.org/10.1016/j.nicl.2016.08.003.
Chennu, S., Stamatakis, E. and Menon, D. (2016). The see-saw brain: recovering consciousness after brain injury. The Lancet Neurology [Online] 15:830-842. Available at: http://dx.doi.org/10.1016/S1474-4422(16)30027-8.
Gonzalez-Gadea, M. et al. (2015). Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder. Journal of Neurophysiology [Online] 114:2625-2636. Available at: http://dx.doi.org/10.1152/jn.00543.2015.
Kirschner, A. et al. (2015). A P300-based cognitive assessment battery. Brain and Behavior [Online] 5:1-14. Available at: http://dx.doi.org/10.1002/brb3.336.
Canales-Johnson, A. et al. (2015). Auditory Feedback Differentially Modulates Behavioral and Neural Markers of Objective and Subjective Performance When Tapping to Your Heartbeat. Cerebral Cortex [Online] 25:4490-4503. Available at: http://dx.doi.org/10.1093/cercor/bhv076.
Naccache, L. et al. (2015). Neural detection of complex sound sequences or of statistical regularities in the absence of consciousness? Brain [Online] 10.109:1-3. Available at: http://dx.doi.org/10.1093/brain/awv190.
Chennu, S. et al. (2014). Spectral signatures of reorganised brain networks in disorders of consciousness. PLoS Computational Biology [Online] 10:e1003887. Available at: http://dx.doi.org/10.1371/journal.pcbi.1003887.
Cruse, D. et al. (2014). The reliability of the N400 in single subjects: Implications for patients with disorders of consciousness. NeuroImage: Clinical [Online] 4:788-799. Available at: http://dx.doi.org/10.1016/j.nicl.2014.05.001.
Gibson, R. et al. (2014). Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography. Clinical Neurophysiology [Online] 125:1556-1567. Available at: http://dx.doi.org/10.1016/j.clinph.2013.11.034.
Chennu, S. et al. (2013). The cost of space independence in P300-BCI spellers. Journal of NeuroEngineering and Rehabilitation [Online] 10:1-13. Available at: http://dx.doi.org/10.1186/1743-0003-10-82.
Chennu, S. et al. (2013). Expectation and Attention in Hierarchical Auditory Prediction. Journal of Neuroscience [Online] 33:11194-11205. Available at: http://dx.doi.org/10.1523/JNEUROSCI.0114-13.2013.
Chennu, S. et al. (2013). Dissociable endogenous and exogenous attention in disorders of consciousness. NeuroImage: Clinical [Online] 3:450-461. Available at: http://dx.doi.org/10.1016/j.nicl.2013.10.008.
Cruse, D. et al. (2013). Reanalysis of "Bedside detection of awareness in the vegetative state: a cohort study" - Authors' reply. Lancet [Online] 381:291-292. Available at: http://dx.doi.org/10.1016/S0140-6736(13)60126-9.
Chennu, S. and Bekinschtein, T. (2012). Arousal modulates auditory attention and awareness: insights from sleep, sedation and disorders of consciousness. Frontiers in Psychology [Online] 3:1-9. Available at: http://dx.doi.org/10.3389/fpsyg.2012.00065.
Cruse, D. et al. (2012). Detecting Awareness in the Vegetative State: Electroencephalographic Evidence for Attempted Movements to Command. PLoS ONE [Online] 7:e49933. Available at: http://dx.doi.org/10.1371%2Fjournal.pone.0049933.
Cruse, D. et al. (2012). Relationship between aetiology and covert cognition in the minimally-conscious state. Neurology [Online] 78:816-822. Available at: http://www.neurology.org/content/78/11/816.
Chatelle, C. et al. (2012). Brain-computer interfacing in disorders of consciousness. Brain Injury [Online] 26:1510-1522. Available at: http://dx.doi.org/10.3109/02699052.2012.698362.
Cruse, D. et al. (2012). Bedside detection of awareness in the vegetative state? - Authors' reply. Lancet [Online] 379:1702. Available at: http://dx.doi.org/10.1016/S0140-6736(12)60715-6.
Cruse, D. et al. (2011). Bedside detection of awareness in the vegetative state: a cohort study. Lancet [Online] 378:2088-2094. Available at: http://dx.doi.org/10.1016/S0140-6736(11)61224-5.
Chennu, S. et al. (2009). Attention Increases the Temporal Precision of Conscious Perception: Verifying the Neural-ST2 Model. PLoS Computational Biology [Online] 5:e1000576. Available at: http://dx.doi.org/10.1371%2Fjournal.pcbi.1000576.
Craston, P. et al. (2009). The attentional blink reveals serial working memory encoding: Evidence from virtual and human event-related potentials. Journal of Cognitive Neuroscience [Online] 21:550-566. Available at: http://dx.doi.org/10.1162/jocn.2009.21036.
Bowman, H. et al. (2008). A Reciprocal Relationship Between Bottom-up Trace Strength and the Attentional Blink Bottleneck: Relating the LC-NE and ST2 Models. Brain Research [Online] 1202:25-42. Available at: http://dx.doi.org/10.1016/j.brainres.2007.06.035.
Conference or workshop item
Chennu, S. et al. (2011). Fortunate Conjunctions Revived: Feature Binding with the 2f-ST2 Model. in: 33rd Annual Meeting of the Cognitive Science Society 2011 (CogSci 2011). Austin, TX: Cognitive Science Society, pp. 2598-2603. Available at: http://www.cs.kent.ac.uk/pubs/2011/3204.
Chennu, S. et al. (2009). The influence of target discriminability on the time course of attentional selection. in: Proceedings of the 31th Annual Conference of the Cognitive Science Society. pp. 1-6.
Bowman, H. et al. (2009). The delayed consolidation hypothesis of all-or-none conscious perception during the attentional blink, applying the ST2 framework. in: Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1-6.
Chennu, S. et al. (2008). Transient Attentional Enhancement during the Attentional Blink: ERP correlates of the ST2 model. in: French, R. M. and Thomas, E. eds. From Associations to Rules: Connectionist Models of Behavior and Cognition. World Scientific Publishing: World Scientific.
Chennu, S. (2008). On Feature Binding in Space and Time. in: University of Kent Postgraduate Conference.
Chennu, S., Habel, K. and Langer, K. (2006). Protected Ethernet Rings for Optical Access Networks. in: Proceedings of the 7th ITG Symposium on Photonic Networks. Berlin: Informationstechnische Gesellschaft, pp. 29-36.
Chennu, S., Habel, K. and Langer, K. (2006). QoS-aware Traffic Protection for Access Rings. in: 11th European Conference on Networks and Optical Communications. pp. 165-174.
Sivanthi, T., Chennu, S. and Kreft, L. (2005). Modeling Decentralized Real-Time Control by State Space Partition of Timed Automata. in: DS-RT '05: Proceedings of the Ninth IEEE International Symposium on Distributed Simulation and Real-Time Applications. IEEE Computer Society, pp. 229-235.
Chennu, S. and Nagaraj, V. (2001). Parallel Computing using Linux Clusters: PFract - A Parallel Fractal Generation Program. in: Computer Society of India technical seminar.
Chennu, S. and Nagaraj, V. (2001). Mobile Ad Hoc Networks. in: Institute of Engineers technical seminar.
Thesis
Chennu, S. (2009). The temporal spotlight of attention: computational and electrophysiological explorations. University of Kent. Available at: http://www.cs.kent.ac.uk/pubs/2009/3054.
Forthcoming
Rivera-Lillo, G. et al. (2018). Reduced delta-band modulation underlies the loss of P300 responses in disorders of consciousness. Clinical Neurophysiology.
Patlatzoglou, K. et al. (2018). Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness. in: The 11th International Conference on Brain Informatics.. Available at: https://uta.engineering/conferences/bi-2018/.
Total publications in KAR: 47 [See all in KAR]

School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

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Last Updated: 20/11/2018