A comparison of t test, F test, and coherence methods of detecting steady-state auditory-evoked potentials, distortion-product otoacoustic emissions, or other sinusoids

Abstract

Sinusoids in background noise can conveniently be detected using unsegmented power spectra, comparing power at the signal frequency to average power at several neighbor frequencies. In this case, the F test is preferable to t tests based on rms or dB values, because of the skewed distributions of rms and dB when signal-to-noise ratio (SNR) = 0. F-test performance improves as the number of frequencies increases, to about 15, but can be degraded if the background noise is not white, with a slope exceeding about 10 dB for the range of frequencies sampled. Segment analysis, using magnitude-squared coherence (MSC) or related statistics, has equivalent statistical power; MSC and F each yield unbiased SNR estimates that have identical distributions when SNR = 0. Selection of F or MSC for detection of sinusoids will usually be a matter of convenience.

Aftereffects of Intense Low-Frequency Sound on Spontaneous Otoacoustic Emissions: Effect of Frequency and Level

“…Five-second windows were further analysed with spectral averaging using Welch's averaged, modified periodogram method employing MatLab pwelch function. SOAEs with a signal-to-noise ratio of less than 5.39 (Dobie and Wilson 1996) were excluded. SOAEs with a frequency between 0.5 and 6 kHz were analysed, as SOAEs with very high frequencies do not show the Bounce (Kugler et al 2014), and the lower-frequency range is limited by the increasing noise floor of the recording system.…”

Objective detection of auditory steady-state responses based on mutual information: Receiver operating characteristics and validation across modulation rates and levels

“…However, as determined by ROC analyses, the most optimal 301 classification of ASSRs is achieved with a criterion MIθ =0.93. Lastly, we showed that MI increases 302 monotonically with increasing number of stimulus presentations (i.e., trials) and can, for some stimulus 303 conditions, detect ASSRs in a fewer number of trials compared to conventional ASSR detection 304 procedures (i.e., F-test; Dobie & Wilson, 1996;John & Picton, 2000). 305…”

“…This was 179 repeated separately for each level and modulation rate. Similarly, we compared the "online" development 180 of MI against the well-known F-test (Dobie & Wilson, 1996;John & Picton, 2000) used in commercial 181 ASSR recording systems (e.g., Bio-logic MASTER II; Intelligent Hearing Systems SmartEP-ASSR). 182…”

“…F-test and magnitude-squared coherence (MSC) (Champlin, 1992;Dobie & Wilson, 1996). In all cases, 75 these statistics become more powerful with increasing number of trials.…”

Frequency-Domain Objective Response Detection Techniques Applied to Evoked Potentials: A Review

“…The Spectral F Test (SFT) is given by the ratio between the Power Spectrum Density (PSD) of the EEG during stimulation y [k] and the background EEG b[k] (Dobie and Wilson, 1996). For windowed EEG signals, the SFT can be estimated by the ratio of the Bartlett periodograms, as follows: The above expression for critical values calculation is not valid for DC (direct current) and Nyquist frequency, since, in these frequencies, the Fourier Transform of EEG epoch leads to purely real values.…”