Li's new paper!
Congratulations to Li for his first publication, which came out in the March issue of NeuroImage.
The paper describes a new technique to detect optimally the N1 wave of laser-evoked potentials (LEPs), even at single-trial level. All this is implemented in an user-friendly Matlab software that can be downloaded in our Software page. The abstract is copied below, and the full text (.pdf) can be downloaded from our Publications page.
Neuroimage. 2010 Mar;50(1):99-111.
A novel approach for enhancing the signal-to-noise ratio and detecting automatically event-related potentials (ERPs) in single trials.
Department of Neuroscience, Physiology and Pharmacology, University College London, UK; Department of Physiology, Anatomy and Genetics, University of Oxford, UK; Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China.
Brief radiant laser pulses can be used to activate cutaneous Adelta and C nociceptors selectively and elicit a number of transient brain responses in the ongoing EEG (N1, N2 and P2 waves of laser-evoked brain potentials, LEPs). Despite its physiological and clinical relevance, the early-latency N1 wave of LEPs is often difficult to measure reliably, because of its small signal-to-noise ratio (SNR), thus producing unavoidable biases in the interpretation of the results. Here, we aimed to develop a method to enhance the SNR of the N1 wave and measure its peak latency and amplitude in both average and single-trial waveforms. We obtained four main findings. First, we suggest that the N1 wave can be better detected using a central-frontal montage (Cc-Fz), as compared to the recommended temporal-frontal montage (Tc-Fz). Second, we show that the N1 wave is optimally detected when the neural activities underlying the N2 wave, which interfere with the scalp expression of the N1 wave, are preliminary isolated and removed using independent component analysis (ICA). Third, we show that after these N2-related activities are removed, the SNR of the N1 wave can be further enhanced using a novel approach based on wavelet filtering. Fourth, we provide quantitative evidence that a multiple linear regression approach can be applied to these filtered waveforms to obtain an automatic, reliable and unbiased estimate of the peak latency and amplitude of the N1 wave, both in average and single-trial waveforms. Copyright © 2009 Elsevier Inc. All rights reserved.
PMID: 20004255 [PubMed - as supplied by publisher]