Latency analysis of single auditory evoked M100 responses by spatio-temporal filtering.

Appropriate spatial filtering followed by temporal filtering is well suited for the single-trial analysis of multi-channel magnetoencephalogram or electroencephalogram recordings. This is demonstrated by the results of a single-trial latency analysis obtained for auditory evoked M100 responses from nine subjects using two different stimulation frequencies. Spatial filters were derived automatically from the data via noise-adjusted principle component analysis, and single-trial latencies were estimated from the signal phase after complex bandpass filtering. For each of the two stimulation frequencies, estimated single-trial latencies were consistent with results obtained from a standard approach using averaged evoked responses. The quality of the estimated single-trial latencies was additionally assessed by their ability to separate between the two different stimulation frequencies. As a result, more than 80% of the single trials can be classified correctly by their estimated latencies.

[1]  Lutz Trahms,et al.  Variability of the QRS signal in high-resolution electrocardiograms and magnetocardiograms , 2001, IEEE Transactions on Biomedical Engineering.

[2]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[3]  Lucas C. Parra,et al.  Recipes for the linear analysis of EEG , 2005, NeuroImage.

[4]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[5]  Y. Adachi,et al.  Magnetoencephalogram systems developed at KIT , 1999, IEEE Transactions on Applied Superconductivity.

[6]  T. Gasser,et al.  Variable latencies of noisy signals: Estimation and testing in brain potential data , 1987 .

[7]  D. Poeppel,et al.  Peri‐threshold encoding of stimulus frequency and intensity in the M100 latency , 1998, Neuroreport.

[8]  Yan Wang,et al.  Contrasting single-trial ERPs between experimental manipulations: Improving differentiability by blind source separation , 2006, NeuroImage.

[9]  P. Jaskowski,et al.  Amplitudes and latencies of single-trial ERP's estimated by a maximum-likelihood method , 1999, IEEE Transactions on Biomedical Engineering.

[10]  D. Liberati,et al.  A parametric method of identification of single-trial event-related potentials in the brain , 1988, IEEE Transactions on Biomedical Engineering.

[11]  R. E. Roger.  A faster way to compute the noise-adjusted principal components transform matrix , 1994, IEEE Trans. Geosci. Remote. Sens..

[12]  T. Sejnowski,et al.  Dynamic Brain Sources of Visual Evoked Responses , 2002, Science.

[13]  L Trahms,et al.  A template-free approach for determining the latency of single events of auditory evoked M100. , 2005, Physics in medicine and biology.

[14]  J. B. Lee,et al.  Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .

[15]  David Poeppel,et al.  Systematic latency variation of the auditory evoked M100: from average to single-trial data , 2004, NeuroImage.

[16]  Hannu Tiitinen,et al.  Auditory event-related responses are generated independently of ongoing brain activity , 2005, NeuroImage.

[17]  Klaus-Robert Müller,et al.  Spatio-spectral filters for improving the classification of single trial EEG , 2005, IEEE Transactions on Biomedical Engineering.

[18]  M. von Spreckelsen,et al.  Estimation of single-evoked cerebral potentials by means of parametric modeling and Kalman filtering , 1988, IEEE Transactions on Biomedical Engineering.

[19]  H. Heinrich,et al.  Single-sweep analysis of event-related potentials by wavelet networks-methodological basis and clinical application , 1999, IEEE Transactions on Biomedical Engineering.

[20]  Qian Du,et al.  Interference and noise-adjusted principal components analysis , 1999, IEEE Trans. Geosci. Remote. Sens..

[21]  W. Klimesch,et al.  Alpha phase synchronization predicts P1 and N1 latency and amplitude size. , 2005, Cerebral cortex.

[22]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.