Including Measures of High Gamma Power Can Improve the Decoding of Natural Speech From EEG

Abstract

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.

The Effects of Speech Masking on Neural Tracking of Acoustic and Semantic Features of Natural Speech

“…The neural processing of continuous speech is often measured by calculated a linear mapping between features of a continuous speech stimulus and the electro- or magnetoencephalographic (EEG/MEG) signals recorded while participants listen to the speech (Crosse et al, 2016; Das et al, 2020; Iotzov, & Parra, 2019; Synigal, et al, 2020). The result of such stimulus-to-neural-response mapping is the temporal response function (TRF; Crosse et al, 2016; Broderick et al, 2018; Crosse et al, 2021).…”

Exploring an EM-algorithm for banded regression in computational neuroscience

“…The dataset includes a ‘broadband’ envelope representation of each speech audio stimulus. The first-order temporal difference of the envelope representations was computed and the output was then half-wave rectified [58, 91, 92]. The envelope features were centered and band-pass filtered between 1 Hz and 8 Hz using a second-order Butterworth filter.…”

Neuronal oscillations: early biomarkers of psychiatric disease?

“…So far, most studies on biomarkers of neuropsychiatric disorders have focused on morphological or molecular parameters (Table 1). Milh et al (2007)] and spindle bursts in mice [modified from Shen and Colonnese (2016)] during early discontinuous activity, as well as filtered γ-oscillation in humans [modified from Synigal et al (2020)] and in mice [modified from Bott et al (2016) One group of proposed NDD biomarkers are components of the microglial signaling cascade. Microglia function as the brain's immune system and they are essential for synaptic refinement, monitoring, pruning, and maintaining synapses throughout development (Magdalon et al, 2020).…”

“…Shared developmental processes (discontinuous activity, γ-activity, myelination) are indicated as age-matched ranges for both species. Insets depict examples of delta-brushes in humans [modified from Milh et al ( 2007 )] and spindle bursts in mice [modified from Shen and Colonnese ( 2016 )] during early discontinuous activity, as well as filtered γ-oscillation in humans [modified from Synigal et al ( 2020 )] and in mice [modified from Bott et al ( 2016 )].…”