Literature DB >> 10380947

Identifying and reducing noise in psychophysiological recordings.

T R Cutmore1, D A James.   

Abstract

Psychophysiology continues to be a widely used methodology in the study of human behaviour, emotion and cognition. The new researcher is faced with a number of problems in the recording process since the desired physiological signal must be isolated from a variety of noise sources. Precautions and strategies that can be implemented in setting up the recording equipment and isolating the subject from interference are described. There are also a number of software techniques that can be applied to improve signal quality after the data have been acquired. An overview is provided of hardware and software methods used to maximise the signal quality.

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Year:  1999        PMID: 10380947     DOI: 10.1016/s0167-8760(99)00014-8

Source DB:  PubMed          Journal:  Int J Psychophysiol        ISSN: 0167-8760            Impact factor:   2.997


  5 in total

1.  Adaptive wavelet filtering for analysis of event-related potentials from the electro-encephalogram.

Authors:  M Browne; T R Cutmore
Journal:  Med Biol Eng Comput       Date:  2000-11       Impact factor: 2.602

2.  Denoising based on spatial filtering.

Authors:  Alain de Cheveigné; Jonathan Z Simon
Journal:  J Neurosci Methods       Date:  2008-04-08       Impact factor: 2.390

3.  Surface facial electromyography, skin conductance, and self-reported emotional responses to light- and season-relevant stimuli in seasonal affective disorder.

Authors:  Kathryn Tierney Lindsey; Kelly J Rohan; Kathryn A Roecklein; Jennifer N Mahon
Journal:  J Affect Disord       Date:  2011-05-19       Impact factor: 4.839

4.  Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

Authors:  William Waugh; John Allen; James Wightman; Andrew J Sims; Thomas A W Beale
Journal:  Comput Math Methods Med       Date:  2018-01-29       Impact factor: 2.238

5.  DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

Authors:  Vernon Lawhern; W David Hairston; Kay Robbins
Journal:  PLoS One       Date:  2013-04-24       Impact factor: 3.240

  5 in total

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