Abstract
This study aimed at evaluating the performance of the Studentized Continuous Wavelet Transform (t-CWT) as a method for the extraction and assessment of event-related brain potentials (ERP) in data from a single subject. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of the t-CWT were assessed and compared to a variety of competing procedures using simulated EEG data at six low signal-to-noise ratios. Results show that the t-CWT combines high sensitivity and specificity with favorable PPV and NPV. Applying the t-CWT to authentic EEG data obtained from 14 healthy participants confirmed its high sensitivity. The t-CWT may thus be well suited for the assessment of weak ERPs in single-subject settings.
Highlights
A wide variety of traumatic and non-traumatic brain injuries can lead to disorders of consciousness (DOC), the vegetative state and the minimally conscious state being the most severe forms (Laureys et al, 2006)
We describe an event-related brain potentials (ERP) detection method based on the continuous wavelet transform (CWT), and compare its performance to a variety of competing analysis techniques in detecting ERP components in artificial and authentic EEG data under varying signal-to-noise ratio (SNR)
We describe and evaluate a variant of the Studentized Continuous Wavelet Transform (t-CWT), in which Student t-values are calculated for each wavelet coefficient (Bostanov and Kotchoubey, 2006) and evaluated using a tmax randomization test (Blair and Karniski, 1993; Groppe et al, 2011)
Summary
A wide variety of traumatic and non-traumatic brain injuries can lead to disorders of consciousness (DOC), the vegetative state (aka. apallic syndrome) and the minimally conscious state being the most severe forms (Laureys et al, 2006). Increasing the number of trials, a method often used to increase the signal-to-noise ratio (SNR), is limited by the rapidly fluctuating vigilance and the short attention span of these patients (Neumann and Kotchoubey, 2004; Laureys et al, 2006). These issues are all the more important, since neuroscientific findings of preserved cognitive functioning in DOC patients may influence the patient’s further medical treatment (Laureys et al, 2006), or questions concerning end-of-life decisions (Eisenberg, 2008). It should show high sensitivity, i.e., correctly identifying those subjects showing the ERP of interest, and high specificity, correctly identifying those subjects who do not show the ERP of interest
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