Article Figures & Data
Figures
- Figure 1.
Stimulus generation, experimental paradigm, and behavioral results. A, AM stimulus generation. The left panel shows the modulation spectra used to generate AM envelops. The line color codes for different modulation spectra. An example of each AM envelope is shown in the middle panel. We filtered the AM envelopes through a bandpass filter of 1–30 Hz and then modulated broadband white noise with the AM envelopes to create AM stimuli. An example waveform of the AM stimuli with a 1/f modulation spectrum of exponent 1 is shown in the upper right panel. The spectrogram of the example AM stimulus is shown in the lower right panel. B, Modulation spectra of AM stimuli. We extracted amplitude envelopes of each AM stimulus and then converted the envelopes to modulation spectra for each AM type. It can be seen that the trends of the prior modulation spectra were preserved in the modulation spectra of the AM stimuli. C, Examples of prior AM envelopes and the AM envelopes from the AM stimuli. The upper row shows examples of the prior AM envelopes and the lower row shows the AM envelopes extracted from the AM stimuli. D, Experimental paradigm for presenting AM stimuli during EEG recording. E, Box plot of behavioral data. D-prime values were calculated to quantify the performance of tone detection. The thin black line indicates the threshold of significance (α level of 0.01) derived from a permutation test (for more details, see Results). F, Local SNR of tones in the AM stimuli. We calculated local SNRs using temporal windows of different sizes. The line color codes for different AM types as in A. G, Correlation between behavioral data and local SNR. The dashed line represents the threshold of significance (α level of 0.01) derived from a permutation test (for more details, see Results). The results show that the shorter the temporal window is, the better the local SNR explains the behavioral performance. The error bars in F, G represent ±1 SE over participants. The AM stimuli can be found in the OSF project folder https://osf.io/yp4k3/.
- Figure 2.
Spatial projection of EEG signals, induced power, and onset/offset responses. A, PCA component extraction and EEG spatial projection. To extract auditory responses across different EEG electrodes, we averaged EEG signals across all the selected trials and calculated the onset response to the stimulus onset (left panel). Five PCA components were extracted across all EEG channels and the first PCA component, which explained the largest variance, was selected. The middle panel shows an example topography of weights of the first PCA component from one participant. We then projected EEG signals of each trial across electrodes to the first PCA component using its weighting matrix and derived signals that summarized auditory-related responses across EEG electrodes. B, Spectrograms of induced power for each type of AM stimuli. From left to right, each spectrogram represents induced power of each AM type. C, Induced power spectra. We averaged induced power from 0.5 to 4.5 s after stimulus onset for each type of AM stimuli to avoid influences of onset and offset responses and motor components caused by button presses. The line color codes for different AM spectra as in Figure 1. The shaded area represents ±1 SE over participants. No significant differences were found between different AM types (p > 0.05). D, Onset and offset responses to each type of AM stimuli. The line color codes for different AM type. No significant differences were found between different AM types (p > 0.05).
- Figure 3.
Encoding framework and results. A, Illustration of encoding framework. The AM stimuli of each AM type and the speech material were used to train TRF models. The TRFs from each AM type were then used to predict neural responses to the other AM types and the speech material. B, Box plot of encoding results of speech signals. We trained encoding models using EEG signals of different frequency bands: full band (1–45 Hz), δ (1–3 Hz), θ (4–7 Hz), α (8–12 Hz), β (13–30 Hz), and γ (31–45 Hz). C, Box plots of encoding results of the AM stimuli. From left to right, each panel shows encoding results from the AM stimuli of each AM type. From B, C, it can be seen that the TRFs trained using the full band and the δ and θ bands better predicted neural responses to both the AM stimuli and the speech material. D, TRF for the speech material. E, TRFs for the AM stimuli. The shaded area represents ±1 SE over participants. F, AM TRFs cross-encoding neural responses to speech signals. We used the TRFs trained from the AM stimuli to predict neural responses to the speech material. A permutation test was preformed to determine which the TRF models from the AM stimuli significantly predict speech neural responses (for more details, see Results). We found that the TRFs from the AM stimuli of 1/f modulation spectra of exponent 1.5 and 2 can best predict speech neural responses in the δ band (p < 0.01). G, Cross-encoding between the AM stimuli. We used the TRFs from the AM stimuli of one AM type to encoding neural responses to the other AM types. From left to right, each confusion matrix represents each neural band. The results along the diagonal show the encoding results of one type of AM stimuli with its own TRF model. A permutation test was preformed to determine significant encoding results for each neural band (for more details, see Results); * represents one-sided α level of 0.05; ** represents one-sided α level of 0.01.
- Figure 4.
Encoding results of different modulation components. A, Encoding results of modulation components from 1 to 45 Hz for the speech material. The line color codes for different neural frequency bands. B, Encoding results of modulation components from 1 to 45 Hz for the AM stimuli. From the left to right, each panel represents different AM envelope types. It can be seen from A, B that the neural responses to the speech material and the AM stimuli can be robustly predicted in the low-frequency neural band (<10 Hz) with its corresponding modulation components. C, Box plot of encoding results of speech signals. D, Box plots of encoding results of the AM stimuli. From left to right, each panel shows encoding results of different neural bands. E, AM TRFs encoding neural responses to speech signals. We found that the TRFs from the AM stimuli of 1/f modulation spectra of exponent 1 can best predict speech neural responses in the δ band (p < 0.01). An effect was also shown for the AM stimuli of 1/f exponent 0.75 in the θ band. F, Cross encoding of the AM stimuli. From left to right, each confusion matrix represents each frequency band. The results along the diagonal show the encoding results of one type of AM stimuli with its own TRF model. A permutation test was preformed to determine significant encoding results for each neural band (for more details, see Results); * represents α level of 0.05; ** represents α level of 0.01.
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