Welcome
I received my BEng degree in electronic engineering from University College Dublin, Ireland, and my PhD degree in signal processing from Imperial College London in 2011, where I am currently a Research Associate. My research interests are in the areas of data fusion, time-frequency analysis, matrix factorisation, complexity analysis and wearable solutions for health monitoring.
In collaboration with Aarhus University, we are developing the Ear-EEG recording concept, which is a fundamentally new approach to wearable technology for brain monitoring.
Contact Details
Dr. David Looney
Communications & Signal Processing Research Group
Dept. of Electronic and Electrical Engineering
Imperial College London
SW7 2BT, UK
Email: david [dot] looney06 [at] imperial [dot] ac [dot] uk
Publications (selected)
Magazine Articles/Journals
V. Goverdovsky, D. Looney, P. Kidmose, C. Papavassiliou, D. P. Mandic,"Co-located multimodal sensing: A robust solution for next generation wearable health," IEEE Sensors Journal, vol. 15, no. 1, pp. 138-145, 2015.
D. Looney, A. Hemakom, D. P. Mandic, "Intrinsic multi-scale analysis: A multivariate empirical mode decomposition framework," Proceedings of the Royal Society of London A, vol. 471, no. 2173, 2014.
A. Williamon, L. Aufegger, D. Wasley, D. Looney, and D. P Mandic, "Complexity of Physiological Responses Decreases in High-Stress Musical Performance," Journal of The Royal Society Interface, vol. 10, no. 89, pp. 20130719, 2013.P. Kidmose, D. Looney, M. Ungstrup, M. L. Rank, and D. P. Mandic, "A Study of Evoked Potentials from Ear-EEG," IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2824-30, 2013.
C. Park, D. Looney, N. Rehman, A. Ahrabian, and D. P. Mandic, "Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 1, pp. 10-22, 2013.
D. Looney, P. Kidmose, C. Park, M. Ungstrup, M. L. Rank, K. Rosenkranz, and D. P. Mandic, "Ear-EEG: User-Centred and Wearable Brain Monitoring," IEEE Pulse Magazine, Nov/Dec, 2012.
D. Looney, M. U. Ahmed, and D. P. Mandic, "Human-Centred Complexity Analysis," Natural Intelligence Magazine, vol. 1, no. 3, pp. 40-42, 2012.
M. Leo, D. Looney, T. D'Orazio and D. P. Mandic, "Identification of Defective Areas in Composite Materials by Bivariate EMD Analysis of Ultrasound," IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 1, pp. 221-232, 2012.
C. Park, D. Looney, and D. P. Mandic, "Time Frequency Analysis of Asymmetry Using EMD," IEEE Transactions on Neural Systems & Rehabilitation Engineering, vol. 19, no. 4, pp. 366-373, 2011.
C. Park, D. Looney, M. Van Hulle, and D. P. Mandic, "The Complex Local Mean Decomposition," Neurocomputing, vol. 74, no. 6, pp. 867-875, 2011.
D. Looney and D. P. Mandic, "Multi-Scale Image Fusion Using Complex Extensions of EMD," IEEE Transactions on Signal Processing, vol. 57, no. 4, pp. 1626-1630, 2009.
Book Chapters
D. Looney, P. Kidmose, and D. P. Mandic, "Ear-EEG: User-Centered and Wearable BCI, State of the art in BCI research," C. Guger Ed., Springer, 2013 (in print).
D. Looney and D. P. Mandic, "Empirical Mode Decomposition For Simultaneous Image Enhancement and Fusion, Image Fusion: Algorithms and Applications," T. Stathaki Ed., Academic Press, pp. 327-342, 2009.
Conference papers
D. Looney, V. Goverdovsky, P. Kidmose, and D. P. Mandic, "Subspace Denoising of EEG Artefacts via Multivariate EMD," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. tbc, 2014.
P. Kidmose, D. Looney, L. Jochumsen, and D. P. Mandic, "Ear-EEG From Generic Earpieces: A Feasibility Study," Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp.543-546, 2013.
P. Kidmose, D. Looney, and D. P. Mandic, "Auditory Evoked Responses from Ear-EEG Recordings," Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp.586-589, 2012.
D. Looney, C. Park, P. Kidmose, M. L. Rank, M. Ungstrup, K. Rosenkranz, and D. P. Mandic, "An In-The-Ear Platform For Recording Electroencephalogram," Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6882-6885, 2011.
D. Looney and D. P. Mandic, "Augmented Complex Matrix Factorisation," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4072-4755, 2011.
P. Kidmose, M. L. Rank, M. Ungstrup, D. Looney, C. Park, and D. P. Mandic, "A Yarbus-Style Experiment to Determine Auditory Attention," Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4650-4653, 2010.
D. Looney, C. Park, Y. Xia, P. Kidmose, M. Ungstrup, and D. P. Mandic, "Towards Estimating Selective Auditory Attention From EEG Using a Novel Time-Frequency-Synchronisation Framework," Proceedings of International Joint Conference on Neural Networks, pp. 1-5, 2010.
D. Looney, C. Park, P. Kidmose, M. Ungstrup, and D. P. Mandic, "Measuring Phase Synchrony Using Complex Extensions of EMD," In: Proceedings of the IEEE Statistical Signal Processing Symposium, pp. 49-52, 2009.
D. Looney and D. P. Mandic, "A Machine Learning Enhanced Empirical Mode Decomposition," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1897-1900, 2008.
D. Looney, L. Li, T. Rutkowski, D. P. Mandic, and A. Cichocki, "Ocular Artifacts Removal from EEG Using EMD," Proceedings of the First International Conference on Cognitive Neurodynamics. pp. 831-835, 2007.
Code
MATLAB Code: Generates R-R interval (RRI) data from raw ECG data
Cardiac output is typically recorded in the form of ECG which contains several clearly identifiable peaks. In particular, the R peaks are dominant sharp peaks in the waveform. Most analysis is performed on a time series derived from the ECG -- the RR interval (RRI) -- which is the time difference between consecutive R peaks.