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

• Main Authors: Shyanthony R. Synigal, Emily S. Teoh, Edmund C. Lalor
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-04-01
• Series: Frontiers in Human Neuroscience
• Subjects: EEG; temporal response function (TRF); high gamma power; decoding attention; speech envelope
• Online Access: https://www.frontiersin.org/article/10.3389/fnhum.2020.00130/full

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.