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"...never shackle thought as the old terms and concepts have!"

James Jerome Gibson

My latest work:

Holmes NP (unpublished) Multisensory integration: inverse effectiveness and noisy signals. Current Biology, 0:0
  [NBArticle #45652] [Cites 1]

Reader A, Tamè L, Holmes NP (unpublished) Two instances of presyncope during magnetic stimulation of the median nerve, and evaluation of resting motor threshold with transcranial magnetic stimulation (tms). , 0:0
  [NBArticle #45584]

Blanchard CCV, McGlashan HL, French B, Sperring RJ, Petrocochino B, Holmes NP (2017) Online control of prehension predicts performance on a standardised motor assessment test in 8-12 year old children. Frontiers in Psychology, 8:374
    [NBArticle #41777] [Cites 45] [CitedBy 7]


(Some of the program scripts I have used in my research, no support is provided, this page is mostly for me and my colleagues)


Versions: 2.3 - 2.4 (23/04/2008) [Matlab, Octave]

Usage: Statistical simulations to study the Law of Inverse Effectiveness in multisensory integration.


Version: (02/12/2008) [Matlab]

Usage: Control of serial and parallel ports for controlling Polhemus Patriot, switching auditory stimuli, signalling to BioSemi EEG interface, generation of auditory stimuli on-line, trial-by-trial and on-line collection, analysis, and display of kinematic data. Filtering & segmenting EOG data, constructing EOG calibration curve. Calibration of Polhemus Patriot using pointer input to provide a model of participants' upper body, head, and head rotations.

History: This was started in November 2007 for controlling my experiments conducted in Dr. Leon Deouell's lab at the Hebrew University of Jerusalem, Israel.


Version: 1.1.3. (31/01/2008) [Matlab]

Usage: Segmentation, sorting, filtering, averaging, and analysis of human electrophysiological data

History: I started writing this in 2006 to process and analyze bio-electrophysiological data (motor-evoked potentials, electromyography, galvanic skin responses) originally recorded from a BioPac system. The program is configured entirely by typing numerous parameters into a series of .m files in Matlab, and run with little input from the Matlab command line. It can be configured for individual blocks of data, multiple blocks, multiple sessions, and multiple subjects. Not very user-friendly, and not very economical programming, but it did the job. Will be updated whenever significant improvements are made.


Version: 1.4 (01/01/2008) [Matlab]

Usage: Simple visual experiment in Matlab, presenting images of hands and asking for self/other or left/right judgments


Version: 1. (25/07/2007) [Matlab]

Usage: Simulation & ANOVA statistical analysis of a published behavioural dataset

History: This program was developed solely for running statistical simulations of a published behavioural dataset, in order to test the alternative hypothesis that the statistical phenomenon of 'regression towards the mean' generated the significant statistical effects.


Version: 1. (12/09/2006) [Matlab]

Usage: Filtering & analysis of single point-to-point reaching movement kinematics

History: Developed to analyse kinematic reaching data under different conditions of visual-proprioceptive mismatch, and the effects of TMS on kinematics.