Concurrent Encoding of Frequency and Amplitude Modulation in Human Auditory Cortex: MEG Evidence

Luo, Huan; Wang, Yadong; Poeppel, David; Simon, Jonathan Z.

doi:10.1152/jn.01256.2005

Cited by 49 publications

(60 citation statements)

References 67 publications

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“…Moreover, although phase locking to FM stimuli has been observed (27)(28)(29), this study shows optimal-phase effects due to phase locking to an auditory stimulus modulated only spectrally (i.e., without any rhythmic amplitude fluctuations). Thus, the current results are suggestive that spectral fluctuations in natural auditory stimuli may act as a pacing signal for low-frequency oscillations and may, in turn, influence auditory perception.…”

Section: Discussionmentioning

confidence: 53%

“…As such, the current study made use of simple nonspeech auditory stimuli, where periodicity was communicated by frequency modulation (FM) rather than amplitude fluctuations (i.e., onsets). Although neural oscillations have been shown to be entrained by FM (27)(28)(29), the possibility that slow FM provides a pacing signal for phase alignment has not received much scientific attention (29).…”

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confidence: 99%

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Frequency modulation entrains slow neural oscillations and optimizes human listening behavior

Henry

Obleser

2012

Proc. Natl. Acad. Sci. U.S.A.

393

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The human ability to continuously track dynamic environmental stimuli, in particular speech, is proposed to profit from "entrainment" of endogenous neural oscillations, which involves phase reorganization such that "optimal" phase comes into line with temporally expected critical events, resulting in improved processing. The current experiment goes beyond previous work in this domain by addressing two thus far unanswered questions. First, how general is neural entrainment to environmental rhythms: Can neural oscillations be entrained by temporal dynamics of ongoing rhythmic stimuli without abrupt onsets? Second, does neural entrainment optimize performance of the perceptual system: Does human auditory perception benefit from neural phase reorganization? In a human electroencephalography study, listeners detected short gaps distributed uniformly with respect to the phase angle of a 3-Hz frequency-modulated stimulus. Listeners' ability to detect gaps in the frequency-modulated sound was not uniformly distributed in time, but clustered in certain preferred phases of the modulation. Moreover, the optimal stimulus phase was individually determined by the neural delta oscillation entrained by the stimulus. Finally, delta phase predicted behavior better than stimulus phase or the event-related potential after the gap. This study demonstrates behavioral benefits of phase realignment in response to frequency-modulated auditory stimuli, overall suggesting that frequency fluctuations in natural environmental input provide a pacing signal for endogenous neural oscillations, thereby influencing perceptual processing.pre-stimulus phase | auditory processing | EEG | FM N eural oscillations are associated with rhythmic fluctuations in the excitation-inhibition cycle of local neuronal populations (1-3). That is, a single neuron is not equally likely to discharge in response to stimulation at all points in time. Instead, its likelihood of responding is influenced by local extracellular and membrane potentials that, in turn, are reflected in neural oscillations. Critically, these neural oscillations can be entrained by external rhythmic sensory stimulation (3, 4) or, less naturally, by rhythmic neural stimulation using transcranial magnetic stimulation (TMS) or transcranial alternating current stimulation (TACS; refs. 5 and 6). As a result, neurons are more likely to fire at temporally expected points in time. Restated, the time of peak neural sensitivity predicts the point in time at which an upcoming stimulus will occur within a framework of continued temporal regularity. Such a rhythmic neural processing mode is adaptive given the abundance of behaviorally relevant environmental auditory stimuli that are inherently rhythmic in nature and, thus, could provide a pacing signal for neural oscillations across a range of frequency bands.Entrainment of low-frequency oscillations involves a reorganization of phase so that the optimal, in this case most excitable, phase comes into line with temporally expected critical events in the ong...

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Section: Discussionmentioning

confidence: 53%

mentioning

confidence: 99%

Frequency modulation entrains slow neural oscillations and optimizes human listening behavior

Henry

Obleser

2012

Proc. Natl. Acad. Sci. U.S.A.

393

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“…Notably, however, we did not observe a 5.075-Hz peak in the analysis of AM-aligned brain signals. The reason for this is that human auditory cortex relies on a phase-coding mechanism to efficiently code complex rhythmic signals containing simultaneous AM and FM (38)(39)(40); critically, the instantaneous phase of the entrained neural oscillation with respect to the AM codes for the instantaneous frequency of the FM.…”

Section: Neural Entrainment By Complex Rhythmic Stimuli Comodulatesmentioning

confidence: 99%

Entrained neural oscillations in multiple frequency bands comodulate behavior

Henry

Obleser

2014

Proc. Natl. Acad. Sci. U.S.A.

207

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Our sensory environment is teeming with complex rhythmic structure, to which neural oscillations can become synchronized. Neural synchronization to environmental rhythms (entrainment) is hypothesized to shape human perception, as rhythmic structure acts to temporally organize cortical excitability. In the current human electroencephalography study, we investigated how behavior is influenced by neural oscillatory dynamics when the rhythmic fluctuations in the sensory environment take on a naturalistic degree of complexity. Listeners detected near-threshold gaps in auditory stimuli that were simultaneously modulated in frequency (frequency modulation, 3.1 Hz) and amplitude (amplitude modulation, 5.075 Hz); modulation rates and types were chosen to mimic the complex rhythmic structure of natural speech. Neural oscillations were entrained by both the frequency modulation and amplitude modulation in the stimulation. Critically, listeners' target-detection accuracy depended on the specific phase-phase relationship between entrained neural oscillations in both the 3.1-Hz and 5.075-Hz frequency bands, with the best performance occurring when the respective troughs in both neural oscillations coincided. Neural-phase effects were specific to the frequency bands entrained by the rhythmic stimulation. Moreover, the degree of behavioral comodulation by neural phase in both frequency bands exceeded the degree of behavioral modulation by either frequency band alone. Our results elucidate how fluctuating excitability, within and across multiple entrained frequency bands, shapes the effective neural processing of environmental stimuli. More generally, the frequency-specific nature of behavioral comodulation effects suggests that environmental rhythms act to reduce the complexity of highdimensional neural states.auditory perception | psychophysics | neuroscience L ow-frequency neural oscillations have recently been proposed to play a crucial role in perception (1-4). This is because lowfrequency neural oscillations reflect fluctuations in local neuronal excitability, meaning they influence the likelihood of neuronal firing in a periodic fashion (4-6). The result is that the probability that a (near-threshold) stimulus will elicit a neuronal response is not a uniform function of time but, instead, depends on the phase of the neural oscillation into which the stimulation falls. Indeed, the dependence of behavioral performance on neural oscillatory phase has been demonstrated in the visual (7-11) and auditory (12-15) domains.The role of neural oscillatory phase in perception is suggested to be of particular importance when stimuli possess temporal regularity (1,11,16,17). Precise alignment of neural oscillations with rhythmic stimuli is accomplished via entrainment, which is a process by which two oscillations become synchronized, or phaselocked, through phase and/or period adjustments (18). Through entrainment, perception of rhythmic stimuli is optimized when high-energy portions of a signal are aligned with periods of high excitab...

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“…Temporal modulations describe the changes in an audio signal in terms of amplitude modulation (AM) and frequency modulation (FM) [22]. It is observed that AM and FM always co-occur and are inseparable features of the audio signal [22]. AM-FM responses can be obtained from the filterbank model of the cochlea.…”

Section: B Teager Energy Operator (Teo)mentioning

confidence: 99%

“…The cepstral coefficients extracted using Gammatone filterbank have been previously used for speech recognition [18]- [20], and non-speech audio classification [21]. Unlike conventional energy (l 2 -norm), Teager Energy Operator (TEO) profile represents both amplitude and frequency variations of a signal [22]. In experiments, we used cepstral features, and spectral features for two classifiers, namely, GMM, and CNN, respectively.…”

Section: Introductionmentioning

confidence: 99%

Novel TEO-based Gammatone features for environmental sound classification

Agrawal

Sailor

Soni

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose to use modified Gammatone filterbank with Teager Energy Operator (TEO) for environmental sound classification (ESC) task. TEO can track energy as a function of both amplitude and frequency of an audio signal. TEO is better for capturing energy variations in the signal that is produced by a real physical system, such as, environmental sounds that contain amplitude and frequency modulations. In proposed feature set, we have used Gammatone filterbank since it represents characteristics of human auditory processing. Here, we have used two classifiers, namely, Gaussian Mixture Model (GMM) using cepstral features, and Convolutional Neural Network (CNN) using spectral features. We performed experiments on two datasets, namely, ESC-50, and UrbanSound8K. We compared TEO-based coefficients with Mel filter cepstral coefficients (MFCC) and Gammatone cepstral coefficients (GTCC), in which GTCC used mean square energy. Using GMM, the proposed TEO-based Gammatone Cepstral Coefficients (TEO-GTCC), and its score-level fusion with MFCC gave absolute improvement of 0.45 %, and 3.85 % in classification accuracy over MFCC on ESC-50 dataset. Similarly, on UrbanSound8K dataset the proposed TEO-GTCC, and its scorelevel fusion with GTCC gave absolute improvement of 1.40 %, and 2.44 % in classification accuracy over MFCC. Using CNN, the score-level fusion of Gammatone spectral coefficient (GTSC) and the proposed TEO-based Gammatone spectral coefficients (TEO-GTSC) gave absolute improvement of 14.10 %, and 14.52 % in classification accuracy over Mel filterbank energies (FBE) on ESC-50 and UrbanSond8K datasets, respectively. This shows that proposed TEO-based Gammatone features contain complementary information which is helpful in ESC task.

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Concurrent Encoding of Frequency and Amplitude Modulation in Human Auditory Cortex: MEG Evidence

Cited by 49 publications

References 67 publications

Frequency modulation entrains slow neural oscillations and optimizes human listening behavior

Frequency modulation entrains slow neural oscillations and optimizes human listening behavior

Entrained neural oscillations in multiple frequency bands comodulate behavior

Novel TEO-based Gammatone features for environmental sound classification

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