JSM 2004 - Toronto

Abstract #300658

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Activity Number: 428
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #300658
Title: Automated Detection of Evoked Responses Using Wavelets
Author(s): Jonathan D. Norton*+
Companies: University of Arkansas for Medical Sciences
Address: 4301 West Markham, Little Rock, AR, 72205,
Keywords: wavelets ; neuroscience ; time-series analysis ; nonstationary ; signal detection
Abstract:

Neural evoked responses, which are studied with such technologies as electro- and magnetoencephalography (EEG and MEG), are traditionally detected by averaging time points around the onset of the stimulus. These averages are analyzed using largely subjective criteria. Automatic, statistically valid detection of evoked responses is complicated by temporal dependence resulting from the high resolution of the EEG/MEG trace. By decorrelating the sensor time series and transforming it toward normality, the discrete wavelet transform (DWT) allows the analyst to test for an association between a stimulus and a sensor time series with appropriate degrees of freedom. Wavelet-based evoked response detection is demonstrated on a study that employed SARA (SQUID Array for Reproductive Assessment), a 151-channel fetal MEG system. Recordings were obtained from pregnant subjects while tones were played against the abdomen. The stimulus time series was compared to the MEG channels by taking the DWT of each. A nonparametric correlation was then computed between the stimulus and MEG wavelet coefficients at an appropriate scale. In this manner, a significance level was determined for each sensor.

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