Dr Andrew Quinn BSc MSc PhD

Dr Andrew Quinn

School of Psychology
Assistant Professor in Psychology

Dr Andrew Quinn develops analysis methods to explore electrophysiological brain signals and how they relate to cognition, individual differences and clinical populations. He develops methods and software for core electrophysiology research as well as advanced methods such as Empirical Mode Decomposition and Hidden Markov Models.

Qualifications

Bsc Psychology - University of York 2010

MSc Cognitive Neuroscience - University of York 2011

PhD Psychology - University of York 2014

Biography

Dr Andrew Quinn completed his PhD in 2014 at the University of York under the supervision of Professor Gary Green and Professor Piers Cornelissen. His thesis, "Neural dynamics in brain networks during the resting state and visual word recognition”, developed methods for estimating time-varying functional connectivity from Magnetoencephalography data. 

After graduating, Dr Quinn moved to Oxford to work as a postdoctoral scientist with Professor Kia Nobre and Professor Mark Woolrich at the Oxford Centre for Human Brain Activity (OHBA). During this time, Dr Quinn developed a range of novel time-series analysis techniques for magnetoencephalography with a particular focus on Empirical Mode Decomposition and Hidden Markov Models. He also plays a major role in the New Therapeutics in Alzheimer’s Disease project which looks to

Following 8 years at OHBA, Dr Quinn started as an Assistant Professor at the University of Birmingham in 2022. His research continues

Research

Electrophysiology (EEG, MEG, OPMs); Neuronal Oscillations; Connectivity; Networks; Perception; Statistics; Software Development

Publications

Recent publications

Article

Seedat, ZA, Rier, L, Gascoyne, LE, Cook, H, Woolrich, MW, Quinn, AJ, Roberts, TPL, Furlong, PL, Armstrong, C, St Pier, K, Mullinger, KJ, Marsh, ED, Brookes, MJ & Gaetz, W 2022, 'Mapping Interictal activity in epilepsy using a hidden Markov model: a magnetoencephalography study', Human Brain Mapping. https://doi.org/10.1002/hbm.26118

Higgins, C, van Es, MWJ, Quinn, AJ, Vidaurre, D & Woolrich, MW 2022, 'The relationship between frequency content and representational dynamics in the decoding of neurophysiological data', NeuroImage, vol. 260, 119462. https://doi.org/10.1016/j.neuroimage.2022.119462

Echeverria-Altuna, I, Quinn, AJ, Zokaei, N, Woolrich, MW, Nobre, AC & van Ede, F 2022, 'Transient beta activity and cortico-muscular connectivity during sustained motor behaviour', Progress in neurobiology, vol. 214, 102281. https://doi.org/10.1016/j.pneurobio.2022.102281

Fabus, MS, Woolrich, MW, Warnaby, CW & Quinn, AJ 2022, 'Understanding harmonic structures through instantaneous frequency', IEEE Open Journal of Signal Processing, vol. 3, pp. 320-334. https://doi.org/10.1109/OJSP.2022.3198012

Fabus, MS, Quinn, AJ, Warnaby, CE & Woolrich, MW 2021, 'Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes', Journal of Neurophysiology, vol. 126, no. 5, pp. 1670-1684. https://doi.org/10.1152/jn.00315.2021

Khawaldeh, S, Tinkhauser, G, Torrecillos, F, He, S, Foltynie, T, Limousin, P, Zrinzo, L, Oswal, A, Quinn, AJ, Vidaurre, D, Tan, H, Litvak, V, Kühn, A, Woolrich, M & Brown, P 2021, 'Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease', Brain, vol. 145, no. 1, pp. 237-250. https://doi.org/10.1093/brain/awab264

Quinn, AJ, Green, GGR & Hymers, M 2021, 'Delineating between-subject heterogeneity in alpha networks with Spatio-Spectral Eigenmodes', NeuroImage, vol. 240, 118330. https://doi.org/10.1016/j.neuroimage.2021.118330

Zhang, S, Cao, C, Quinn, A, Vivekananda, U, Zhan, S, Liu, W, Sun, B, Woolrich, M, Lu, Q & Litvak, V 2021, 'Dynamic analysis on simultaneous iEEG-MEG data via hidden Markov model', NeuroImage, vol. 233, 117923. https://doi.org/10.1016/j.neuroimage.2021.117923

Quinn, AJ, Lopes-Dos-Santos, V, Dupret, D, Nobre, AC & Woolrich, MW 2021, 'EMD: Empirical Mode Decomposition and Hilbert-Huang spectral analyses in Python', The Journal of Open Source Software, vol. 6, no. 59, 2977. https://doi.org/10.21105/joss.02977

Zokaei, N, Quinn, AJ, Hu, MT, Husain, M, Van Ede, F & Nobre, AC 2021, 'Reduced cortico-muscular beta coupling in Parkinson's disease predicts motor impairment', Brain Communications, vol. 3, no. 3, fcab179. https://doi.org/10.1093/braincomms/fcab179

Juan, CH, Nguyen, KT, Liang, WK, Quinn, AJ, Chen, YH, Muggleton, NG, Yeh, JR, Woolrich, MW, Nobre, AC & Huang, NE 2021, 'Revealing the dynamic nature of amplitude modulated neural entrainment with Holo-Hilbert spectral analysis', Frontiers in Neuroscience, vol. 15, 673369. https://doi.org/10.3389/fnins.2021.673369

Bauer, A-KR, Ede, FV, Quinn, AJ & Nobre, AC 2021, 'Rhythmic modulation of visual perception by continuous rhythmic auditory stimulation', The Journal of Neuroscience, vol. 41, no. 33, pp. 7065-7075. https://doi.org/10.1523/JNEUROSCI.2980-20.2021

Quinn, AJ, Lopes-dos-Santos, V, Huang, N, Liang, W-K, Juan, C-H, Yeh, J-R, Nobre, AC, Dupret, D & Woolrich, MW 2021, 'Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics', Journal of Neurophysiology, vol. 126, no. 4, pp. 1190-1208. https://doi.org/10.1152/jn.00201.2021

Preprint

Quinn, AJ, Atkinson, LZ, Gohil, C, Kohl, O, Pitt, J, Zich, C, Nobre, AC & Woolrich, MW 2022 'The GLM-Spectrum: a multilevel framework for spectrum analysis with covariate and confound modelling' bioRxiv. https://doi.org/10.1101/2022.11.14.516449

Short survey

Zich, C, Quinn, AJ, Mardell, LC, Ward, NS & Bestmann, S 2020, 'Dissecting Transient Burst Events', Trends in Cognitive Sciences, vol. 24, no. 10, pp. 784-788. https://doi.org/10.1016/j.tics.2020.07.004

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