Analyzing EEG Signals in Auditory Speech Comprehension Using Temporal Response Functions and Generalized Additive Models

Kimberley Mulder, Louis ten Bosch, Lou Boves


Analyzing EEG signals recorded while participants are listening to continuous speech with the purpose of testing linguistic hypotheses is complicated by the fact that the signals simultaneously reflect exogenous acoustic excitation and endogenous linguistic processing. This makes it difficult to trace subtle differences that occur in mid-sentence position. We apply an analysis based on multivariate temporal response functions to uncover subtle mid-sentence effects. This approach is based on a per-stimulus estimate of the response of the neural system to speech input. Analyzing EEG signals predicted on the basis of the response functions might then bring to light conditionspecific differences in the filtered signals. We validate this approach by means of an analysis of EEG signals recorded with isolated word stimuli. Then, we apply the validated method to the analysis of the responses to the same words in the middle of meaningful sentences.


 DOI: 10.21437/Interspeech.2018-1676

Cite as: Mulder, K., ten Bosch, L., Boves, L. (2018) Analyzing EEG Signals in Auditory Speech Comprehension Using Temporal Response Functions and Generalized Additive Models. Proc. Interspeech 2018, 1452-1456, DOI: 10.21437/Interspeech.2018-1676.


@inproceedings{Mulder2018,
  author={Kimberley Mulder and Louis {ten Bosch} and Lou Boves},
  title={Analyzing EEG Signals in Auditory Speech Comprehension Using Temporal Response Functions and Generalized Additive Models},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={1452--1456},
  doi={10.21437/Interspeech.2018-1676},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1676}
}