Assessment of auditory threshold using Multiple Magnitude-Squared Coherence and amplitude modulated tones monaural stimulation around 40 Hz

Multichannel search strategy for improving the detection of auditory steady-state response

Auditory steady-state response (ASSR) is useful for hearing threshold estimation. The ASSR is usually detected with objective response detectors (ORD). The performance of these detectors depends on the signal-to-noise ratio (SNR) as well as the signal length. Since it is undesirable to increase the signal length, then, this work provides a multivariate technique for improving the SNR and consequently the detection power. We propose the insertion of a short calibration step before the detection protocol, in order to perform a search among the available electroencephalogram (EEG) derivations and select the derivation with the highest SNR. The ORD used in this work was the magnitude-squared coherence (MSC). The standard detection protocol is to use the same EEG derivation in all exams. Using 22-scalp positions, the new technique achieved a detection rate higher than that obtained in 99.13% of the standard detection protocol. When restrictions were applied to the search, a superior performance was achieved. Thus, the technique proposed was able to track the best EEG derivations before exams and seems to be able to deal with the variability between individuals and between sessions.

The global Beta test for hidden periodicities in signals and its extensions to multivariate systems

BACKGROUND AND OBJECTIVE

There are many phenomena that lead to changes in the power spectrum of a given signal, and their detection has been a challenge that has received considerable attention over the years. Objective Response Detection (ORD) techniques are a set of tools that perform automated tests for such a task, allowing thus to automatically track changes in the spectrum. The performance of these detectors is affected by the signal-to-noise ratio (SNR) of the recorded signal as well as the length of the available data. The Global F Test (GFT) is a promising detector that can be used to test whether there is a statistically significant difference between the spectrum before and during an event. In fact, this detector has proved useful in the detection of event-related desynchronization/synchronization (ERD/ERS), where only amplitude, but not the phase, changes are locked to the stimulus. In order to improve the statistical power of the GFT (for the same length of recording), multiple channels recorded simultaneously can be included. This concept is called Multivariate Response Detection. The aim of the current work is to extend the GFT to the multivariate (multichannel) case.

METHODS

Firstly, the single channel normalization of the GFT is presented as a new ORD detector - the global Beta test (GBT). After that, three multivariate extensions of this new test are derived. The critical values used in the detection of spectral changes are obtained by using theoretical distributions, and where this is intractable, by means of Monte Carlo simulations. The probability of detection (PD) of each technique was estimated using simulation and was used in order to compare the detectors performance. A practical example with the electroencephalogram (EEG) from 10 volunteers under intermittent photic stimulation was also provided.

RESULTS

The statistics under both the null and alternative hypothesis could be obtained for all detectors. Simulated results for PD demonstrate the strong potential of the proposed method and the performances in EEG data are always improved with increasing number of signals.

CONCLUSION

If more than one signal is available, then the multivariate extensions may provide significant benefit compared to the original GFT.

A bayesian approach to the spectral F-Test: Application to auditory steady-state responses

BACKGROUND AND OBJECTIVE

Auditory steady-state responses (ASSRs) represent an objective method used in clinical practice to assess hearing thresholds. The steady-state nature of these signals allows response detection by means of statistical techniques in the frequency domain as spectral F-test. This objective response detection (ORD) compares the power of the response bin against the power of the neighboring frequency noise bins. Most ORD algorithms are based on the Neyman-Pearson approach to the hypothesis test provided that the likelihood ratio test is the most powerful test for a given significance level alpha (also called Type I error). On the other hand, the Bayesian approach allows the inclusion of prior information in the model and enables the updating of this information with posterior knowledge. This approach, however, has not been explored with respect to ORD techniques, thus enabling the exploration of new paradigms, which may contribute to this field of study, especially in terms of the time required for response detection. The aim of this study is to use the Bayesian approach in the implementation of the spectral F-test for application to ASSRs.

METHODS

Monte Carlo simulations were performed to evaluate Neyman-Pearson and Bayesian detectors' performances with the spectral F-test as a function of the signal-to-noise ratio. Then, the two detectors were applied to ASSR recordings of nine normal-hearing individuals subjected to amplitude-modulated tones of various intensities.

RESULTS

Both simulation and ASSR data analyses showed that among the scenarios analyzed, the most promising case was that in which the lowest possible values for the a priori probability were selected for the null hypothesis (H0), allowing detection at low signal-to-noise ratios. The worst performance occurred when the a priori probabilities for both hypotheses were equal. The ASSR data also showed that higher stimulus intensity led to better performance and faster detection due to improvements in the signal-to-noise ratio.

CONCLUSIONS

The a priori probabilities can affect the Bayesian detector's performance, directly impacting the time needed to identify responses. The parallel behaviors observed between the performances of both approaches showed that the Bayesian detector can achieve its ideal performance at lower signal-to-noise ratios compared to the optimal performance of the Neyman-Pearson detector, reflecting the promising applicability of the Bayesian approach to evoked potentials.