Human Frequency-following Response Research Articles

Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization.

Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better (p < .05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial.

The frequency-following response (FFR) provides enriched information on how acoustic stimuli are processed in the human brain. Based on recent studies, machine learning techniques have demonstrated great utility in modeling human FFRs. This tutorial focuses on the fundamental principles, algorithmic designs, and custom implementations of several supervised models (linear regression, logistic regression, k -nearest neighbors, support vector machines) and an unsupervised model ( k -means clustering). Other useful machine learning tools (Markov chains, dimensionality reduction, principal components analysis, nonnegative matrix factorization, and neural networks) are discussed as well. Each model's applicability and its pros and cons are explained. The choice of a suitable model is highly dependent on the research question, FFR recordings, target variables, extracted features, and their data types. To promote understanding, an example project implemented in Python is provided, which demonstrates practical usage of several of the discussed models on a sample dataset of six FFR features and a target response label.

Individual differences in human frequency-following response predict pitch labeling ability

The frequency-following response (FFR) provides a measure of phase-locked auditory encoding in humans and has been used to study subcortical processing in the auditory system. While effects of experience on the FFR have been reported, few studies have examined whether individual differences in early sensory encoding have measurable effects on human performance. Absolute pitch (AP), the rare ability to label musical notes without reference notes, provides an excellent model system for testing how early neural encoding supports specialized auditory skills. Results show that the FFR predicts pitch labelling performance better than traditional measures related to AP (age of music onset, tonal language experience, pitch adjustment and just-noticeable-difference scores). Moreover, the stimulus type used to elicit the FFR (tones or speech) impacts predictive performance in a manner that is consistent with prior research. Additionally, the FFR predicts labelling performance for piano tones better than unfamiliar sine tones. Taken together, the FFR reliably distinguishes individuals based on their explicit pitch labeling abilities, which highlights the complex dynamics between sensory processing and cognition.

Meter enhances the subcortical processing of speech sounds at a strong beat

The temporal structure of sound such as in music and speech increases the efficiency of auditory processing by providing listeners with a predictable context. Musical meter is a good example of a sound structure that is temporally organized in a hierarchical manner, with recent studies showing that meter optimizes neural processing, particularly for sounds located at a higher metrical position or strong beat. Whereas enhanced cortical auditory processing at times of high metric strength has been studied, there is to date no direct evidence showing metrical modulation of subcortical processing. In this work, we examined the effect of meter on the subcortical encoding of sounds by measuring human auditory frequency-following responses to speech presented at four different metrical positions. Results show that neural encoding of the fundamental frequency of the vowel was enhanced at the strong beat, and also that the neural consistency of the vowel was the highest at the strong beat. When comparing musicians to non-musicians, musicians were found, at the strong beat, to selectively enhance the behaviorally relevant component of the speech sound, namely the formant frequency of the transient part. Our findings indicate that the meter of sound influences subcortical processing, and this metrical modulation differs depending on musical expertise.

Response properties of the human frequency-following response (FFR) to speech and non-speech sounds: level dependence, adaptation and phase-locking limits

Objective: The frequency-following response (FFR) is a neurophonic potential used to assess auditory neural encoding at subcortical stages. Despite the FFR’s empirical and clinical utility, basic response properties of this evoked potential remain undefined.Design: We measured FFRs to speech and nonspeech (pure tone, chirp sweeps) stimuli to quantify three key properties of this potential: level-dependence (I/O functions), adaptation and the upper limit of neural phase-locking.Study sample: n = 13 normal-hearing listeners.Results: I/O functions showed FFR amplitude increased with increasing stimulus presentation level between 25 and 80 dB SPL; FFR growth was steeper for tones than speech when measured at the same frequency. FFR latency decreased 4–5 ms with decreasing presentation level from 25 and 80 dB SPL but responses were ∼2 ms earlier for speech than tones. FFR amplitudes showed a 50% reduction over 6 min of recording with the strongest adaptation in the first 60 s (250 trials). Estimates of neural synchronisation revealed FFRs contained measurable phase-locking up to ∼1200–1300 Hz, slightly higher than the single neuron limit reported in animal models.Conclusions: Findings detail fundamental response properties that will be important for using FFRs in clinical and empirical applications.

Quality of frequency-following response to speech sounds linked with left prefrontal hemodynamic activity using fNIRS+EEG

Quality of frequency-following response to speech sounds linked with left prefrontal hemodynamic activity using fNIRS+EEG

Sonification of scalp-recorded frequency-following responses (FFRs) offers improved response detection over conventional statistical metrics

BackgroundThe human frequency-following response (FFR) is a neurophonic potential used to examine the brain’s encoding of complex sounds (e.g., speech) and monitor neuroplastic changes in auditory processing. Given the FFR’s low amplitude (order of nanovolts), current conventions in literature recommend collecting several thousand trials to obtain a robust evoked response with adequate signal-to-noise ratio. New methodBy exploiting the spectrotemporal fidelity of the response, we examined whether auditory playbacks (i.e., “sonifications”) of the neural FFR could be used to assess the quality of running recordings and provide a stopping rule for signal averaging. Results and comparison with existing methodIn a listening task over headphones, naïve listeners detected speech-evoked FFRs within ∼500 sweeps based solely on their perception of the presence/absence of a tonal quality to the response. Moreover, response detection based on aural sonifications offered similar and in some cases a 2–3× improvement over objective statistical techniques proposed in the literature (i.e., MI, SNR, MSC, F-test, Corr). ConclusionsOur findings suggest that simply listening to FFR responses (sonifications) might offer a rapid technique to monitor real-time EEG recordings and provide a stopping rule to terminate signal averaging that performs comparably or better than current approaches.

Frequency-dependent fine structure in the frequency-following response: The byproduct of multiple generators

The frequency-following response (FFR) is an auditory-evoked response recorded at the scalp that captures the spectrotemporal properties of tonal stimuli. Previous investigations report that the amplitude of the FFR fluctuates as a function of stimulus frequency, a phenomenon thought to reflect multiple neural generators phase-locking to the stimulus with different response latencies. When phase-locked responses are offset by different latencies, constructive and destructive phase interferences emerge in the volume-conducted signals, culminating in an attenuation or amplification of the scalp-recorded response in a frequency-specific manner. Borrowing from the literature on the audiogram and otoacoustic emissions (OAEs), we refer to this frequency-specific waxing and waning of the FFR amplitude as fine structure. While prior work on the human FFR was limited by small sets of stimulus frequencies, here, we provide the first systematic investigation of FFR fine structure using a broad stimulus set (90 + frequencies) that spanned the limits of human pitch perception. Consistent with predictions, the magnitude of the FFR response varied systematically as a function of stimulus frequency between 16.35 and 880 Hz. In our dataset, FFR high points (local maxima) emerged at ∼44, 87, 208, and 415 Hz with FFR valleys (local minima) emerging ∼62, 110, 311, and 448 Hz. To investigate whether these amplitude fluctuations are the result of multiple neural generators with distinct latencies, we created a theoretical model of the FFR that included six putative generators. Based on the extant literature on the sources of the FFR, our model adopted latencies characteristic of the cochlear microphonic (0 ms), cochlear nucleus (∼1.25 ms), superior olive (∼3.7 ms), and inferior colliculus (∼5 ms). In addition, we included two longer latency putative generators (∼13 ms, and ∼25 ms) reflective of the characteristic latencies of primary and non-primary auditory cortical structures. Our model revealed that the FFR fine structure observed between 16.35 and 880 Hz can be explained by the phase-interaction patterns created by six generators with relative latencies spaced between 0 and 25 ms. In addition, our model provides confirmatory evidence that both subcortical and cortical structures are activated by low-frequency (<100 Hz) tones, with the cortex being less sensitive to frequencies > 100 Hz. Collectively, these findings highlight (1) that the FFR is a composite response; (2) that the FFR at any given frequency can reflect activity from multiple generators; (3) that the fine-structure pattern between 16.35 and 880 Hz is the collective outcome of short- and long-latency generators; (4) that FFR fine structure is epiphenomenal in that it reflects how volume-conducted electrical potentials originating from different sources with different latencies interact at scalp locations, not how these different sources actually interact in the brain; and (5) that as a byproduct of these phase-interaction patterns low-amplitude responses will emerge at some frequencies, even when the underlying generators are fully functioning. We believe these findings call for a re-examination of how FFR amplitude is interpreted in both clinical and experimental contexts.

Neural Correlates of the Binaural Masking Level Difference in Human Frequency-Following Responses.

The binaural masking level difference (BMLD) is an auditory phenomenon where binaural tone-in-noise detection is improved when the phase of either signal or noise is inverted in one of the ears (SπNo or SoNπ, respectively), relative to detection when signal and noise are in identical phase at each ear (SoNo). Processing related to BMLDs and interaural time differences has been confirmed in the auditory brainstem of non-human mammals; in the human auditory brainstem, phase-locked neural responses elicited by BMLD stimuli have not been systematically examined across signal-to-noise ratio. Behavioral and physiological testing was performed in three binaural stimulus conditions: SoNo, SπNo, and SoNπ. BMLDs at 500Hz were obtained from 14 young, normal-hearing adults (ages 21-26). Physiological BMLDs used the frequency-following response (FFR), a scalp-recorded auditory evoked potential dependent on sustained phase-locked neural activity; FFR tone-in-noise detection thresholds were used to calculate physiological BMLDs. FFR BMLDs were significantly smaller (poorer) than behavioral BMLDs, and FFR BMLDs did not reflect a physiological release from masking, on average. Raw FFR amplitude showed substantial reductions in the SπNo condition relative to SoNo and SoNπ conditions, consistent with negative effects of phase summation from left and right ear FFRs. FFR amplitude differences between stimulus conditions (e.g., SoNo amplitude-SπNo amplitude) were significantly predictive of behavioral SπNo BMLDs; individuals with larger amplitude differences had larger (better) behavioral B MLDs and individuals with smaller amplitude differences had smaller (poorer) behavioral B MLDs. These data indicate a role for sustained phase-locked neural activity in BMLDs of humans and are the first to show predictive relationships between behavioral BMLDs and human brainstem responses.

Multichannel recordings of the human brainstem frequency-following response: Scalp topography, source generators, and distinctions from the transient ABR

Brainstem frequency-following responses (FFRs) probe the neural transcription of speech/music, auditory disorders, and plasticity in subcortical auditory function. Despite clinical and empirical interest, the response's neural basis remains poorly understood. The current study aimed to more fully characterize functional properties of the human FFR (topography, source locations, generation). Speech-evoked FFRs were recorded using a high-density (64 channel) electrode montage. Source dipole modeling and 3-channel Lissajous analysis was used to localize the most likely FFR generators and their orientation trajectories. Additionally, transient auditory brainstem responses (ABRs), recorded in the same listeners, were used to predict FFRs and test the long-held assumption that the sustained potential reflects a series of overlapping onset responses. Results showed that FFRs were maximal at frontocentral scalp locations with obliquely oriented sources from putative generators in the midbrain (i.e., upper brainstem). Comparisons between derived and actual recordings revealed the FFR is not a series of repeated ABR wavelets and thus, represents a functionally distinct brainstem response. FFRs recorded at temporal electrode sites showed larger amplitudes and contained higher frequency components than vertex channels (Fz, Cz) suggesting that FFRs measured near the mastoid are generated more peripherally (auditory nerve) than measurements at frontocentral scalp locations. Furthermore, this reveals the importance of choice in reference electrode location for FFR interpretation. Our findings provide non-invasive evidence that (i) FFRs reflect sustained neural activity whose sources are consistent with rostral brainstem generators and (ii) FFRs are functionally distinct from the onset ABR response.

An automated procedure for detecting human frequency-following responses to voice pitch

The frequency-following response (FFR) to voice pitch has been widely examined in research laboratories and has demonstrated its potential to be transformed into a useful tool for patients with hearing, speech, and language disorders in the clinic. During the past decade, many aspects of the FFR have been reported. The presence of such a response, however, still relies on subjective interpretation of the observer. Aside from a recent study reporting two algorithms for detecting such a response, there has been limited number of studies reporting the development of an automated procedure for FFR. The purpose of this study is (1) to develop an automated procedure that utilizes the statistical properties of the temporal and spectral energy distributions in the recorded waveforms and (2) to explore the effectiveness, accuracy, and efficiency of the automated procedure and compare them with those obtained from conventional algorithms and human judgments.

Effects of silent interval on human frequency-following responses to voice pitch

Human frequency-following responses (FFR) to voice pitch have provided valuable information on how the human brainstem processes speech information. Recordings of the FFR to voice pitch, however, may overlap when insufficient silent intervals are used. To determine the shortest silent interval that can be used with no overlap between adjacent response waveforms, FFRs were recorded from twelve Chinese adults using a wide range of silent intervals. The stimulus token was a Chinese monosyllable with a rising pitch of 117 to 166 Hz and a duration of 250 ms. A high stimulus intensity at 70 dB SPL was used to maximize overlaps in the response waveforms. A total of seven silent intervals, ranging from the full length of the stimulus duration down to approximately half period of the fundamental frequency of the stimulus token, were administered at a random order across participants. Two distinct methods (Hilbert transform and root-mean-square amplitudes) were used to delineate the envelopes and overlaps of the response waveforms. A one-way repeated measures ANOVA was significant (p=0.038) in defining the magnitude of overlaps for the 10 ms pre-stimulus interval. The results indicated the shortest silent interval that could be used without compromising the response is 35-45 ms.

Relative Power of Harmonics in Human Frequency-Following Responses Associated with Voice Pitch in American and Chinese Adults

When the fundamental frequency (f0) is removed from a complex stimulus, the pitch of the f0 is still perceived by the listener. Through the use of the scalp-recorded frequency-following response, this study examined the relative contributions of thef0 and its harmonics in pitch processing by systematically manipulating the speech stimulus to remove component frequencies. 12 American and 12 Chinese adults were recruited. There were statistically significant effects of pitch strength and frequency error for the experimental-condition factor. There were significantly larger responses to the harmonics-only conditions than those obtained in the f0-only and control conditions. No statistically significant difference was observed between the two groups of participants. These findings indicate that neural responses associated with individual harmonics dominate the pitch processing in the human brainstem, irrespective of whether the listener's native language is nontonal or tonal.

Exponential modeling of human frequency-following responses to voice pitch

Objective: Recent studies have shown that the frequency-following response (FFR) to voice pitch can be a useful method to evaluate the signal-processing mechanisms and neural plasticity in the human brainstem. The purpose of this study was to examine the quantitative properties of the FFR trends with an exponential curve-fitting model. Design: FFR trends obtained with increasing number of sweeps (up to 8000 sweeps) at three stimulus intensities (30, 45, and 60 dB nHL) were fit to an exponential model that consisted of estimates of background noise amplitude, asymptotic response amplitude, and a time constant. Five objective indices (Frequency Error, Slope Error, Tracking Accuracy, Pitch Strength and RMS Ratio) were used to represent different perspectives of pitch processing in the human brainstem. Study Sample: Twenty-three native speakers (16 males; age = 24.7 ± 2.1 years) of Mandarin Chinese were recruited. Results: The results demonstrated that the exponential model provided a good fit (r2 = 0.89 ± 0.10) to the FFR trends with increasing number of sweeps for the five objective indices. Conclusions: The exponential model, combined with the five objective indices, can be used for difficult-to-test patients and may prove to be useful as an assessment and diagnostic method in both clinical and basic research efforts.SumarioObjetivo: Estudios recientes han mostrado que la respuesta de seguimiento de frecuencia (FFR) al tono de la voz puede ser un método útil para evaluar los mecanismos de procesamiento de la señal y la plasticidad neural en el tallo cerebral humano. El propósito de este estudio fue examinar las propiedades cuantitativas de las tendencias del FFR con un modelo exponencial de curva de adaptación. Diseño: Las tendencias de la FFR obtenidas con un número creciente de barridos (hasta 8000 barridos), a tres intensidades diferentes (30, 45 y 60 dB nHL) fueron introducidas en un modelo exponencial que consistió en estimados de la amplitud del ruido de fondo, amplitud asintótica de respuesta y una constante de tiempo. Cinco índices objetivos (Error de frecuencia, error de pendiente, exactitud de seguimiento, fortaleza del tono y tasa RSM) fueron utilizados para representar diferentes perspectivas del procesamiento del tono en el tallo cerebral del humano. Muestra del estudio: Se reclutaron treinta y tres hablantes naturales (16 hombres; edades = 24,7 ± 2.1 años) del chino mandarín. Resultados: Los resultados demostraron que el modelo exponencial brindó una buena adaptación (r2 = 0.89 ± 0.10) a la tendencias de la FFR con un número creciente de barridos para los cinco índices objetivos. Conclusiones: El modelo exponencial, combinado con los cinco índices objetivos, puede ser utilizado en pacientes difíciles de evaluar y demostrar su utilidad como un método de evaluación y diagnóstico, tanto en protocolos clínicos como de investigación.

Noise tolerance in human frequency-following responses to voice pitch

Speech communication usually occurs in the presence of background noise. This study examined noise tolerance in the brainstem's processing of voice pitch, as reflected by the scalp-recorded frequency-following response (FFR) from 12 normal-hearing adults. By systematically manipulating signal-to-noise ratio (SNR) across three different stimulus intensities, the results indicated that Frequency Error, Slope Error, and Tracking Accuracy remained relatively stable until SNR was degraded to 0 dB or lower (i.e., a turning point). This turning point not only provided physiological evidence supporting pitch tolerance of noise but also allowed recommendation of a minimal SNR when evaluating pitch processing in difficult-to-test patients.

Evaluation of two algorithms for detecting human frequency-following responses to voice pitch

Objective. Voice pitch carries important cues for speech perception in humans. Recent studies have shown the feasibility of recording the frequency-following response (FFR) to voice pitch in normal-hearing listeners. The presence of such a response, however, has been dependent on subjective interpretation of experimenters. The purpose of this study was to develop and test an automated procedure including a control-experimental protocol and response-threshold criteria suitable for extracting FFRs to voice pitch, and compare the results to human judgments. Design. A set of four Mandarin tones (Tone 1 flat; Tone 2 rising; Tone 3 dipping; and Tone 4 falling) were prepared to reflect the four contrastive pitch contours. Two distinctive algorithms, short-term autocorrelation in the time domain and narrow-band spectrogram in the frequency domain, were used to estimate the Frequency Error, Slope Error, Tracking Accuracy, Pitch Strength and Pitch-Noise Ratio of the recordings from individual listeners as well as the power and false-positive rates of each algorithm. Study Sample. Eleven native speakers (five males; age: mean ± SD = 31.4 ± 4.7 years) of Mandarin Chinese were recruited. Results. The results demonstrated that both algorithms were suitable for extracting FFRs and the objective measures showed comparable results to human judgments. Conclusions. The automated procedure used in this study, including the use of the control-experimental protocol and response thresholds used for each of the five objective indices, can be used for difficult-to-test patients and may prove to be useful as an assessment and diagnostic method in both clinical and basic research efforts.SumarioLa altura tonal de la voz contiene importantes claves para la percepción del habla en humanos. Estudios recientes mostraron la factibilidad de registrar la respuesta de seguimiento a la frecuencia (FFR), de la frecuencia vocal en normo-oyentes. No obstante, la presencia de esta respuesta ha sido dependiente de la interpretación subjetiva de los experimentadores. El propósito de este estudio fue desarrollar y probar un procedimiento automatizado, incluyendo un protocolo de control experimental y criterios de respuesta-umbral, apropiados para la extracción de FFRs de la frecuencia vocal y comparar los resultados con los juicios humanos. Se preparó un set de tonos del Mandarín (tono 1, plano; tono 2, ascendente; tono, 3 silbado y tono 4, descendente), para mostrar los cuatro contornos de frecuencia contrastante. Se usaron dos algoritmos distintivos, de auto-correlación a corto plazo, para estimar el error en la frecuencia y en el gradiente, la exactitud del rastreo, la fortaleza de la frecuencia y la relación tono-ruido de los registros de los oyentes individuales así como de la fuerza y las tasas de falsos positivos de cada algoritmo. Los resultados demostraron que ambos algoritmos fueron apropiados para extraer las FFRs y que las mediciones objetivas mostraron resultados comparables a los de los juicios humanos.

Aging alters the perception and physiological representation of frequency: Evidence from human frequency-following response recordings

Older adults, even with clinically normal hearing sensitivity, have auditory perceptual deficits relative to their younger counterparts. This difficulty may in part, be related to a decline in the neural representation of frequency. The purpose of this study was to examine the effect of age on behavioral and physiological measures of frequency representation. Thirty two adults (ages 22–77), with hearing thresholds ⩽25 dB HL at octave frequencies 0.25–8.0 kHz, participated in this experiment. Frequency discrimination difference limens (FDLs) were obtained at 500 and 1000 Hz using a two-interval, two-alternative forced choice procedure. Linear regression analyses showed significant declines in FDLs at both frequencies as age increased. Frequency-following responses (FFRs) were elicited by 500 and 1000 Hz tonebursts, as well as at frequencies within and outside those FDLs. Linear regression of FFR phase coherence and FFR amplitude at frequencies at and slightly below 1000 Hz showed significant decreases as age increased. Therefore, pitch discrimination, as measured by FDLs, and neural representation of frequency, as reflected by FFR, declined as age increased. Although perception and neural representation concurrently declined, one was not predictive of the other.

Human Frequency-Following Response to Speech-Like Sounds: Correlates of Off-Frequency Masking

Off-frequency masking of the second formant by energy at the first formant has been shown to influence both identification and discrimination of the second formant in normal-hearing and hearing-impaired listeners. While both excitatory spread and two-tone suppression have been implicated in this simultaneous masking, their relative contribution has been shown to depend on both the level of the masker and the frequency separation between the probe and the masker. Off-frequency masking effects were evaluated in 10 normal-hearing human adults using the frequency-following response (FFR) to two two-tone approximations of vowel stimuli (/a/ and /u/). In the first experiment, the masking effect of F₁ on F₂ was evaluated by attenuating the level of F₁ relative to a fixed F₂ level. In the second experiment, the masking effect was evaluated by increasing the frequency separation between F₁ and F₂ using F₂ frequency as the variable. Results revealed that both attenuation of the F₁ level, and increasing the frequency separation between F₁ and F₂ increased the magnitude of the FFR component at F₂. These results are consistent with a release from off-frequency masking. Given that the results presented here are for high signal and masker levels and for relatively smaller frequency separation between the masker and the probe, it is possible that both suppression and excitatory spread contributed to the masking effects observed in our data.

Human Frequency-Following Responses to Binaural Masking Level Difference Stimuli

Binaural masking level difference is the behavioral threshold difference between a diotic condition (SoNo) and a dichotic condition with a 180 degrees interaural phase delay of either the signal (SpiNo) or the masker (SoNpi). Threshold disparity is partially related to coincidence-detecting units in the medial superior olive that are sensitive to low-frequency binaural stimuli with interaural phase differences. Previous surface evoked potential studies report significant latency and amplitude differences to SpiNo stimuli with respect to SoNo stimuli in the P1-N1 auditory event related potential, but no study has reported physiologic masking level differences in a brain stem evoked potential. The human frequency-following response (FFR) represents activity from low-frequency, phase locking neural units in the upper brainstem. Unmasked FFRs to 500 Hz tone bursts and masked FFRs using a 1.5 kHz low-pass masker were recorded from nine normal-hearing adult subjects. Significant reduction in FFR amplitude occurred in the SoNo condition, re the So condition, with masker intensities near the psychoacoustic SoNo masking level. Significant FFR amplitude recovery was observed for both the SoNpi and SpiNo conditions. These results support the role of phase-locked neural activity in brainstem mechanisms involved in perceptual masking release.

Human frequency-following response correlates of the distortion product at 2F1-F2.

We characterized the 2F1-F2 distortion product reflected in the human frequency-following response (FFR). In the first experiment, we evaluated the input-output growth functions of the distortion product at 2F1-F2 (FFR-DP) for three primary pairs. In the second experiment, we tested the effect of primary tone level variation on the FFR-DP. The results for all three stimulus pairs showed that while the amplitude of FFR-DP increased with stimulus intensity, the slope of the amplitude growth decreased with increasing frequency. Consistent with distortion product otoacoustic emission (DPOAE) data, our observations suggest that there is a distinct region where the separation of the primary tone levels produces maximal distortion. The robust FFR-DP measure could complement the less reliable DPOAE at low frequencies and when middle ear pathology precludes its measurement.