The Fundamental and Applied Electroencephalography laboratory in the Brain Research & Imaging Centre (BRIC) will include stationary and mobile electroencephalography (EEG) systems.  
This cutting-edge facility will provide an integrated research environment where EEG can be combined with other neuroimaging and neurostimulation techniques to further investigate the mechanisms behind visual and social cognition, and help facilitate further funding and collaboration opportunities.
Perceptual, motor, cognitive, and social abilities are critical for people’s interactions with the physical and social world in their everyday lives.
Electroencephalography (EEG) measures electrical activity in the brain with high temporal resolution. This means that we can find out more about the neural processes that support these abilities, advancing not only basic research but, ultimately, research in mental health and other applied fields.

Investigating the neural bases of perceptual, motor, cognitive, and social abilities

EEG Laboratory Lead, Dr Giorgio Ganis has been using electroencephalography (EEG), brain stimulation (TMS), functional magnetic resonance imaging (fMRI), as well as behavioural method, to study the neuroscience of perceptual, motor, cognitive, and social abilities.
Over the years, his research has resulted in influential and highly-cited studies and has attracted grants from both research and applied funding bodies. Recent research has revolved around two related areas of visual and social cognition: the psychology and neuroscience of imagination, and the psychology and neuroscience of perspective-taking and deceptive communication.
Research on imagination is especially important for health-related fields, with potential applications going from addressing various mental health problems to developing brain-machine interfaces.
Research on the neuroscience of deceptive communication is especially relevant for security-related fields, given the centrality of trust within existing social, financial, and technological systems.
BRIC Director, Professor Stephen Hall, has been using electro-and magneto-physiological measures (EEG and MEG) as well as fMRI and TMS to characterise the neural network mechanisms, such as oscillatory processes, underlying cognitive and behavioural function in health and disease. Recent research has primarily focussed on the neuroscience of sensorimotor function.
Professor Jeremy Goslin has been adopting a multidisciplinary research approach to investigate both the behaviour and the cognitive neuroscience of topics in language, economics, and trust. His current research has centred around using behavioural and EEG methods to investigate areas such as the neuroscience of reinforcement learning, tool use in virtual reality, and language and language development.
Fundamental and Applied Electroencephalography Laboratory logo
EEG lab

Key publications

Ward E, Ganis G & Bach P 2019. Spontaneous vicarious perception of the content of others’ visual perspective. Current Biology 29(5) 874-880.

Hsu CW, Begliomini C, Dall'Acqua T, Ganis G 2019. The effect of mental countermeasures on neuroimaging-based concealed information tests. Human Brain Mapping 40(10):2899-2916.

Zabelina DL, Ganis G 2018. Creativity and cognitive control: Behavioral and ERP evidence that divergent thinking, but not real-life creative achievement, relates to better cognitive control. Neuropsychologia 118, 20-28.

Ganis G, Bridges D, Hsu CW, Schendan HE 2016. Is anterior N2 enhancement a reliable electrophysiological index of concealed information? Neuroimage 143, 152-165.

Battaglini L, Casco C, Isaacs BR, Bridges D & Ganis G 2016. Electrophysiological correlates of motion extrapolation: An investigation on the CNV. Neuropsychologia. 95, 86-93.

Battaglini, L., Casco, C., Isaacs, B. R., Bridges, D., & Ganis, G. (2017). Electrophysiological correlates of motion extrapolation: An investigation on the CNV. Neuropsychologia, 95, 86-93.

Ganis, G., Bridges, D., Hsu, C. W., & Schendan, H. E. (2016). Is anterior N2 enhancement a reliable electrophysiological index of concealed information? Neuroimage, 143, 152-165.

Ganis, G., & Kutas, M. (2003). An electrophysiological study of scene effects on object identification. Brain Res Cogn Brain Res, 16(2), 123-144.

Ganis, G., Kutas, M., & Sereno, M. I. (1996). The search for "common sense": an electrophysiological study of the comprehension of words and pictures in reading. J Cogn Neurosci, 8(2), 89-106.

Ganis, G., & Schendan, H. E. (2008). Visual mental imagery and perception produce opposite adaptation effects on early brain potentials. Neuroimage, 42(4), 1714-1727.

Ganis, G., & Schendan, H. E. (2012). Concealed semantic and episodic autobiographical memory electrified. Front Hum Neurosci, 6, 354.

Ganis, G., Smith, D., & Schendan, H. E. (2012). The N170, not the P1, indexes the earliest time for categorical perception of faces, regardless of interstimulus variance. Neuroimage, 62(3), 1563-1574.

Schendan, H. E., & Ganis, G. (2012). Electrophysiological potentials reveal cortical mechanisms for mental imagery, mental simulation, and grounded (embodied) cognition. Front Psychol, 3, 329.

Schendan, H. E., & Ganis, G. (2013). Face-specificity is robust across diverse stimuli and individual people, even when interstimulus variance is zero. Psychophysiology, 50(3), 287-291.

Schendan, H. E., Ganis, G., & Kutas, M. (1998). Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms. Psychophysiology, 35(3), 240-251.

Zabelina, D. L., & Ganis, G. (2018). Creativity and cognitive control: Behavioral and ERP evidence that divergent thinking, but not real-life creative achievement, relates to better cognitive control. Neuropsychologia, 118(Pt A), 20-28.

Sambrook, T.D, Roser, M., Goslin, J. (2012). Prospect theory does not describe the feedback-related negativity value function. Psychophysiology, 49(12), 1533-44.

Marrett, N. E., de-Wit, L. H., Roser, M. E., Kentridge, R. W., Milner, A. D., & Lambert, A. J. (2011). Testing the dorsal stream attention hypothesis: Electrophysiological correlates and the effects of ventral stream damage. Visual Cognition, 19(9), 1089-1121. (THIS COMBINES fMRI, PATIENTS AND EEG).

Roser, M.E., Fugelsang, J., Handy, T.C., Dunbar, K.N., & Gazzaniga, M.S. (2009). Representations of physical plausibility revealed by event-related potentials. NeuroReport, 20,1081-1086.

Rhodes E. Gaetz W, Marsden J and Hall SD. (2018). Transient alpha and beta synchrony underlies preparatory recruitment of directional motor networks. Journal of Cognitive Neuroscience, 0(6):867-875. doi: 10.1162/jocn_a_01250.

Gaetz W, Rhodes E, Bloy L, Blaskey L, Jackel CR, Brodkin ES, Waldman A, Embick D, Hall S, Roberts TP. (2019). Evaluating motor cortical oscillations and age-related change in autism spectrum disorder. Neuroimage. 11:116349. doi: 10.1016/j.neuroimage.2019.116349.

Prokic E., Woodhall, GL, Williams AC, Stanford IM, Hall SD. (2019). Bradykinesia is driven by cumulative beta power during continuous movement and alleviated by GABAergic modulation in Parkinson’s disease. Frontiers in Neurology 10: 1298.

Hall SD, Prokic EJ, McAllister CJ, Ronnqvist KC, Williams AC, Witton C, Woodhall GL, Stanford IM.(2014). GABA-mediated changes in inter-hemispheric beta frequency activity in early-stage Parkinson’s disease. Neuroscience 281 :68-76.

Rossiter HE, Worthen SF, Hall SD & Furlong PL. (2013). Gamma Oscillatory Amplitude EncodesStimulus Intensity in Primary Somatosensory Cortex. Frontiers in Human Neuroscience. 15;7:362

Ronnqvist KC, McAllister CJ, Woodhall GL, Stanford & Hall SD. (2013). A multimodal perspectiveon the composition of cortical oscillations. Frontiers in Human Neuroscience. 7, 132.

Mcallister CJ, Ronnqvist KC, Woodhall GL, Stanford IM, Furlong PL & Hall SD. (2013). OscillatoryBeta Activity Mediates Neuroplastic Effects of Motor Cortex Stimulation in Humans. Journal of Neuroscience 33(18):7919-7927

Hall SD, Stanford IM, Yamawaki N, McAllister CJ, Rönnqvist KC, Woodhall GL & Furlong PL.(2011) The role of GABAergic modulation in motor function related neuronal network activity. NeuroImage. 56(3):1506-10.

Worthen SF, Furlong PL, Hall SD, Aziz Q & Hobson AR. (2011) Primary and secondary somatosensory cortex responses to anticipation and pain: a magnetoencephalography study. European Journal of Neuroscience. 33(5): 946-59

Hall SD, Yamawaki N, Fisher AE, Clauss RP, Woodhall GL & Stanford IM. (2010). Desynchronisation of pathological low-frequency brain activity by the hypnotic drug zolpidem. Clinical Neurophysiology. 121(4): 549-55.

Hall SD, Barnes GR, Furlong PL, Seri S & Hillebrand A. (2010) Neuronal network pharmacodynamics of GABAergic modulation in the human cortex determined using pharmaco-MEG. Human Brain Mapping. 31(4): 581-94.

Sambrook, T.D., Wills, A.J., Hardwick, B., & Goslin, J. (2018). Model-free and model-based reward prediction errors in EEG. NeuroImage, 178, 162-171.

Wills, A.J., Lavric, A., Hemmings, Y., & Surrey, E. (2014). Attention, predictive learning, and the inverse base-rate effect: Evidence from event-related potentials. NeuroImage, 87, 61-71.

Wills, A.J., Lavric, A., Croft, G., & Hodgson, T.L. (2007). Predictive learning, prediction errors and attention: Evidence from event-related potentials and eye tracking. Journal of Cognitive Neuroscience, 19, 843-854.