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Speech is central to communication among humans. Meaning is largely conveyed by the selection of linguistic units such as words, phrases and sentences. However, prosody, that is the variation of acoustic cues that tie linguistic segments together, adds another layer of meaning. There are various features underlying prosody, one of the most important being pitch and how it is modulated. Recent fMRI and ECoG studies have suggested that there are cortical regions for pitch which respond primarily to resolved harmonics and that high-gamma cortical activity encodes intonation as represented by relative pitch. Importantly, this latter result was shown to be independent of the cortical tracking of the acoustic energy of speech, a commonly used measure. Here, we investigate whether we can isolate low-frequency EEG indices of pitch processing of continuous narrative speech from those reflecting the tracking of other acoustic and phonetic features. Harmonic resolvability was found to contain unique predictive power in delta and theta phase, but it was highly correlated with the envelope and tracked even when stimuli were pitch-impoverished. As such, we are circumspect about whether its contribution is truly pitch-specific. Crucially however, we found a unique contribution of relative pitch to EEG delta-phase prediction, and this tracking was absent when subjects listened to pitch-impoverished stimuli. This finding suggests the possibility of a separate processing stream for prosody that might operate in parallel to acoustic-linguistic processing. Furthermore, it provides a novel neural index that could be useful for testing prosodic encoding in populations with speech processing deficits and for improving cognitively controlled hearing aids.

The human auditory system is highly skilled at extracting and processing information from speech in both single-speaker and multi-speaker situations. A commonly studied speech feature is the amplitude envelope which can also be used to determine which speaker a listener is attending to in those multi-speaker situations. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) has shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such processing is strongly reflected in the power of high frequency neural activity (around 70-150 Hz; known as high gamma). The first aim of this study was to determine if high gamma power scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Specifically, linear regression was used to investigate speech envelope and attention decoding in low frequency EEG, high gamma power EEG, and in both EEG signals combined. The second aim was to assess whether the information reflected in high gamma power EEG may be complementary to that reflected in well-established low frequency EEG indices of speech processing. Exploratory analyses were also completed to examine how low frequency and high gamma power EEG may be sensitive to different features of the speech envelope. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in some subjects. This same pattern was true for attention decoding using a separate group of subjects who participated in a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power EEG, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Our results indicated that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects. Combining high gamma power and low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

Humans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using functional magnetic resonance imaging, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical frequency and modulation statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brain’s selectivity for speech and music in other populations. Significance Statement In other species, ecologically important sounds such as vocalizations generate larger responses in the auditory cortex than other types of natural sounds, but it is unclear if this is true for humans. We recorded EEG as subjects listened to various natural sounds. Classification analysis based on the EEG responses as well as analysis of the average power of the EEG responses showed increased activation in the brain to speech and music sounds compared to other natural sounds. This study demonstrates the human brain’s selectivity to speech and music sounds and suggests a cost-effective and efficient way to study the brain’s selectivity for speech and music in other populations.

Objective It has been shown that attentional selection in a simple dichotic listening paradigm can be decoded offline by reconstructing the stimulus envelope from single-trial neural response data. Here, we test the efficacy of this approach in an environment with non-stationary talkers. We then look beyond the envelope reconstructions themselves and consider whether incorporating the decoder values-which reflect the weightings applied to the multichannel EEG data at different time lags and scalp locations when reconstructing the stimulus envelope-can improve decoding performance. Approach High-density EEG was recorded as subjects attended to one of two talkers. The two speech streams were filtered using HRTFs, and the talkers were alternated between the left and right locations at varying intervals to simulate a dynamic environment. We trained spatio-temporal decoders mapping from EEG data to the attended and unattended stimulus envelopes. We then decoded auditory attention by (1) using the attended decoder to reconstruct the envelope and (2) exploiting the fact that decoder weightings themselves contain signatures of attention, resulting in consistent patterns across subjects that can be classified. Main results The previously established decoding approach was found to be effective even with non-stationary talkers. Signatures of attentional selection and attended direction were found in the spatio-temporal structure of the decoders and were consistent across subjects. The inclusion of decoder weights into the decoding algorithm resulted in significantly improved decoding accuracies (from 61.07% to 65.31% for 4 s windows). An attempt was made to include alpha power lateralization as another feature to improve decoding, although this was unsuccessful at the single-trial level. Significance This work suggests that the spatial-temporal decoder weights can be utilised to improve decoding. More generally, looking beyond envelope reconstruction and incorporating other signatures of attention is an avenue that should be explored to improve selective auditory attention decoding.