Some improvements in the measurement of variable latency acoustically evoked potentials in human EEG

Evaluation of a technique to measure latency jitter in event-related potentials

Auditory selective attention results in larger event-related potentials (ERPs) than those recorded to unattended stimuli. Larger ERPs arise from either a greater number of neurons being stimulated or the same number of neurons with better synchrony. The synchrony aspect was studied in an attention experiment recording the N1 response and showed that the latency jitter was significantly less (p=0.002) for the responses to attended stimuli compared with those to unattended stimuli. Here the improved technique to measure latency jitter is the focus of study. Simulated responses were used to evaluate the technique's performance at different signal-to-noise ratios (SNRs) and compare it to the standard technique. Checks that the technique had performed satisfactorily in each case were achieved and a measure of reliability for each result was developed. The parameters defining the technique were varied and the optimum values chosen. This new technique opens the way for researchers to investigate the latency properties of lower SNR ERPs and gives a new insight into auditory selective attention.

Selective attention increases the temporal precision of the auditory N100 event-related potential

Selective attention increases the amplitude of the averaged N100 event-related potential (ERP). This increase may result from more neurons responding to the stimulus or from the same number of neurons better synchronised with the stimulus, or both. We investigated the synchronization mechanism using a new response latency jitter measurement algorithm that performed well for all the signal-to-noise ratios obtained in the experiment. We found that the significantly increased N100 amplitude is accounted for by a significantly decreased latency jitter variance for the attended stimuli.

An automatic sequential recognition method for cortical auditory evoked potentials

The detection of cortical auditory evoked potentials (CAEP), which are part of the electroencephalogram (EEG) in reaction to acoustic stimuli, has important applications such as determining objective audiograms. The detection is usually performed by a human operator, with support from often basic signal processing methods. This paper presents a novel mechanism for the detection of CAEPs, which is fully automatic and stops the measurement when a given confidence is reached. This proposed detector comprises of three stages. First, a feature extraction by a wavelet transform parameterizes the time domain EEG signal by only few transform coefficients. This feature vector is then classified by a neural network which yields a binary vote on every EEG segment. Finally, a sequential statistical test is performed on successive classifications; this stops the measurement if a specified decision confidence has been reached. The adjustment of the detector according to a clinical database is discussed. Thus adjusted, the proposed CAEP detection scheme is applied to a study, and compared with a human operator. The results demonstrate that this method can attain similar results, but outperforms the human expert for stimulation levels close to the hearing threshold.

Subspace regularization method for the single-trial estimation of evoked potentials

A method for the single-trial estimation of the evoked potentials is proposed. The method is based on the so-called subspace regularization approach in which the second-order statistics of the set of the measurements is used to form a prior information model for the evoked potentials. The method is closely related to the Bayesian estimation. The performance of the proposed method is evaluated using realistic simulations. As a specific application the method is applied to the estimation of the target responses in the P300 test.

Some considerations on estimating event-related brain signals

Understanding the timing of mental acts is one of the prominent questions in information processing research. The analysis of event related potentials (ERP) with their high temporal resolution might make access to cognition related brain activity possible. We consider three major problems which make the application of ERPs questionable and then propose some solutions to these problems. The primary problem concerns the separation of the ERPs from the background EEG which is not related to the stimulus. The most common method used is averaging. We argue that this is not the most appropriate method and suggest an alternative for estimating the signal in single-trial recordings. Artifacts present a second problem. We will first review established methods of dealing with eye-movement artifacts and then propose an alternative. We will also report on current work on the parametrisation of single-trial signal estimates, which constitute the third problem considered.

P3 latency jitter assessed using 2 techniques. I. Simulated data and surface recordings in normal subjects

Latency variability measurement using cross-correlational techniques has the drawback of alignment to background noise not related to ERP activity. We compared latency jitter estimation in simulated and real P3 recordings using Woody's algorithm and a non-cross-correlational technique, the maximum likelihood technique (MLT). Simulated ERPs (with introduced latency jitter) were generated using either a 1/2 cycle 2 Hz sine wave or an averaged P3 ERP with 1 of 3 added noise types in 5 signal to noise ratios (SNRs): (i) white noise; (ii) a 10 Hz sine wave; (iii) a 7.5 Hz sine wave. Jitter measurement accuracy was assessed using mean square error (MSE) for 1 iteration of the Woody method and each of 4 iterations of the MLT. Lowest MSEs occurred for higher SNRs and 1 iteration of the MLT. The MLT and Woody method were applied to P3 ERPs of 13 subjects with SNRs greater than 0.4 P3 latency jitter was significantly lower for the MLT. Latency jitter (both methods) did not differ between homologous electrodes and was highest in posterior electrodes. In the latency corrected ERP data of subjects with persistent alpha activity periodic components occurred in the Woody corrected average (not seen in the conventional or the MLT corrected averages). Our data indicate that the MLT is the more accurate method for determining latency jitter.

Adaptive Fourier series modeling of time-varying evoked potentials: study of human somatosensory evoked response to etomidate anesthetic

Evoked potentials (EPs) have traditionally been analyzed in time domain, with amplitude and latency of various signal components used in clinical interpretation. A new approach, called adaptive Fourier series modeling (FSM), is presented here. Dynamic changes in magnitudes of Fourier coefficients are analyzed for diagnostic purposes. In order to estimate the time-varying changes in the Fourier coefficients of noisy signals, a least mean-square filtering algorithm is applied. Results of computer simulations as well as experimental data are presented. Time-varying trends are presented in a new compressed evoked spectrum format. These techniques are applied to the study of alterations in human somatosensory EPs caused by the intravenous administration of etomidate during neurosurgical procedures. Amplitude increases of the order of 200-500% occurring within a time span of about 100 sec were captured. Due to its superior convergence properties, the adaptive FSM technique estimates more rapid changes in amplitude and latency than exponentially weighted averaging or moving window averaging schemes.