title: An automated approach towards detecting complex behaviours in deep brain oscillations.
abstract: Extracting event-related potentials (ERPs) from neurological rhythms is of fundamental importance in neuroscience research. Standard ERP techniques typically require the associated ERP waveform to have low variance, be shape and latency invariant and require many repeated trials. Additionally, the non-ERP part of the signal needs to be sampled from an uncorrelated Gaussian process. This limits methods of analysis to quantifying simple behaviours and movements only when multi-trial data-sets are available. We introduce a method for automatically detecting events associated with complex or large-scale behaviours, where the ERP need not conform to the aforementioned requirements. The algorithm is based on the calculation of a detection contour and adaptive threshold. These are combined using logical operations to produce a binary signal indicating the presence (or absence) of an event with the associated detection parameters tuned using a multi-objective genetic algorithm. To validate the proposed methodology, deep brain signals were recorded from implanted electrodes in patients with Parkinson's disease as they participated in a large movement-based behavioural paradigm. The experiment involved bilateral recordings of local field potentials from the sub-thalamic nucleus (STN) and pedunculopontine nucleus (PPN) during an orientation task. After tuning, the algorithm is able to extract events achieving training set sensitivities and specificities of [87.5 ± 6.5, 76.7 ± 12.8, 90.0 ± 4.1] and [92.6 ± 6.3, 86.0 ± 9.0, 29.8 ± 12.3] (mean ± 1 std) for the three subjects, averaged across the four neural sites. Furthermore, the methodology has the potential for utility in real-time applications as only a single-trial ERP is required.
authors.affiliation: Department of Mechanical Engineering, Imperial College London, London, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Mace
authors.affiliation: Department of Medicine, Imperial College London, London, UK.
authors.forename: Nada
authors.initials: N
authors.lastname: Yousif
authors.affiliation: Department of Medicine, Imperial College London, London, UK.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Naushahi
authors.affiliation: Holland Bloorview Kids Rehabilitation Hospital, Toronto, and Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.
authors.forename: Khondaker
authors.initials: K
authors.lastname: Abdullah-Al-Mamun
authors.affiliation: Institute of Sound and Vibration Research, University of Southampton, Southampton, UK.
authors.forename: Shouyan
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Medicine, Imperial College London, London, UK.
authors.forename: Dipankar
authors.initials: D
authors.lastname: Nandi
authors.affiliation: Department of Mechanical Engineering, Imperial College London, London, UK; Department of Systems Engineering, Naval Postgraduate School, Monterey, CA, USA. Electronic address: r.vaidyanathan@imperial.ac.uk.
authors.forename: Ravi
authors.initials: R
authors.lastname: Vaidyanathan
mesh_terms: D000465:Algorithms; D001519:Behavior / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D046690:Deep Brain Stimulation / Q000379:methods*; D004569:Electroencephalography; D001330:Electronic Data Processing; D005793:Generalization, Psychological / Q000502:physiology; D006801:Humans; D009068:Movement / Q000502:physiology*; D010507:Periodicity
keywords: Deep brain implants; Event detection; Event-related potentials; Movement related potentials; Pedunculopontine nucleus (PPN); Sensory motor control; Sub-thalamic nucleus (STN)