Using correlation analysis to assess the reliability of evoked potential components identified by signal averaging

BACKGROUND Signal averaging is the conventional method of enhancing the signal-tonoise ratio in recordings of evoked potentials (EPs) from the skin surface in humans. However there may be difficulties in reliably identifying the features of interest especially when a low number of trials are averaged or when large artefacts with similar waveform as the signal are present. NEW METHOD This method uses the median cross-correlation coefficient of every possible pair of recording repetitions within user-defined windows across the time course of the evoked potential. This is shown to be a good surrogate of signal-to-noise ratio and can be superimposed on the averaged trace to provide reliability information for all components of interest. RESULTS We applied our method both to simulated signals embedded within the noise component of EP recordings and to real examples of somatosensory EPs. We demonstrated that it can assess the reliability of recorded components independently from their amplitude and could identify artefacts which mimicked genuine components. COMPARISON WITH EXISTING METHODS There have been a number of previous approaches to this problem but none has found widespread support. This method adds additional information to a common existing technique and is easy to interpret and apply. CONCLUSIONS This method is used as a visual adjunct to the existing interpretation of averaged evoked potentials and will allow judgements of the reliability of each observed component to be made. This is particularly valuable for situations where few repetitions are possible such as nociceptive evoked potentials, which are of increasing clinical interest.

[1]  R.Quian Quiroga,et al.  Obtaining single stimulus evoked potentials with wavelet denoising , 2000, nlin/0006027.

[2]  R. Coppola,et al.  Signal to noise ratio and response variability measurements in single trial evoked potentials. , 1978, Electroencephalography and clinical neurophysiology.

[3]  Rajesh Patel,et al.  Effective extraction of evoked potentials using template cross correlation , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[4]  C Elberling,et al.  Objective detection of averaged auditory brainstem responses. , 1984, Scandinavian audiology.

[5]  Andreas Keil,et al.  Assessing the internal consistency of the event-related potential: An example analysis. , 2017, Psychophysiology.

[6]  G. Dawson,et al.  CEREBRAL RESPONSES TO ELECTRICAL STIMULATION OF PERIPHERAL NERVE IN MAN , 1947, Journal of neurology, neurosurgery, and psychiatry.

[7]  J. Lefaucheur Clinical neurophysiology of pain. , 2019, Handbook of clinical neurology.

[8]  Johannes Sarnthein,et al.  High test–retest reliability of checkerboard reversal visual evoked potentials (VEP) over 8 months , 2009, Clinical Neurophysiology.

[9]  McGillem Cd,et al.  Signal processing in evoked potential research: applications of filtering and pattern recognition. , 1981 .

[10]  D K Prasher,et al.  Latency variability and temporal interrelationships of the auditory event-related potentials (N1, P2, N2, and P3) in normal subjects. , 1986, Electroencephalography and clinical neurophysiology.

[11]  S Turner,et al.  Extraction of short-latency evoked potentials using a combination of wavelets and evolutionary algorithms. , 2003, Medical engineering & physics.

[12]  B Bromm,et al.  Principal component analysis of pain-related cerebral potentials to mechanical and electrical stimulation in man. , 1982, Electroencephalography and clinical neurophysiology.

[13]  Peter E Clayson,et al.  ERP Reliability Analysis (ERA) Toolbox: An open-source toolbox for analyzing the reliability of event-related brain potentials. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[14]  Jorge I. Aunon,et al.  Classification and Detection of Single Evoked Brain Potentials Using Time-Frequency Amplitude Features , 1986, IEEE Transactions on Biomedical Engineering.