Title:Measuring complexity in EEG in response to speech stimuli
Research Objectives:
Non-invasive objective tests of auditory function are needed for the early diagnosis of hearing disorders and in
the development of the next generation of prosthetic devices. This project will investigate the use of
nonlinear measures of complexity in the electroencephalogram (EEG), in response to speech stimuli. It is
hoped this may yield a sensitive measure of the neural encoding of speechto be developed as a research and
clinical tool.
Background and motivation:
Any form of hearing loss – even when audibility is compensated for with a hearing aid or the person is fitted
with a cochlear implant (CI)1 – reduces the ability to encode speech (or sound in general) at the auditory
periphery. This results from the impairment itself and the signal processing within the auditory prosthetic, and
produces a bottleneck to the flow of information into the auditory neural system. Another type of hearing
impairment, known as central auditory processing disorder (CAPD), arises from abnormal neural
representation of speech and non-speech sounds in the central auditory nervous system (rather than at the
sensory periphery).Its presence is not necessarily accompanied by a corresponding loss of hearing sensitivity,
and thus its diagnosis is often much later in a child’s development.Both the loss of sensitivity and CAPD lead to
difficulties in various auditory functions, important for listening and comprehending spoken language,
especially in noisy backgrounds. Any significant deficit in the comprehension of spoken language can
negatively impact upon an individual’s education, social development and quality of life.
Non-invasive objective tests of auditory function are needed in the early diagnosis of such hearing disorders as
earlier intervention typically leads to better clinical outcomes. Further,in the development of auditory
prosthetic devices such as hearing aids and cochlear implants, it would be beneficial to have an objective
measure sensitive to the underlying neural network that processes sound, to help identify bottlenecks to the
flow of auditory information. The nonlinear complexity of the electroencephalogram (EEG) is believed to
contain information about the architecture and function of the underlying neural networks in the brain over
many time scales (Bosl et al., 2011). Complexity has already been successfully used to measure the differential
maturation of the auditory and visual systems (Lippe et al., 2009).This project will investigate the potential for
modified multi-scale entropy (mMSE, Bosl et al., 2011; Xie et al., 2008; Costa et al., 2005) as a new objective
metric for assessing degraded neural representation of speech stimuli. Information is carried in the envelope
of speech signals over multiple time scales, corresponding to changes representing phonemic and syllabic
transitions, highly important for speech intelligibility. It is already known that the auditory cortex follows, or
consistently reacts to, the changes in the envelope of speech stimuli (Aiken and Picton, 2008; Cogan and
Poeppel, 2011). It remains to be tested whether entropy measured across multiple time scales is sensitive to
the presence of neural representation of speech.
Milestones:
1.
2.
3.
Develop a numerical simulation of speech evoked responses embedded in 1/f noise at different
powers, to determine the robustness of the complexity measure to poor SNR.
Carry out short and simple experiment [approx. 2 weeks, supported by researchers at the IDH] to
collect resting state EEG from normal hearing subjects and when clean speech stimuli is presented.
Investigate the difference in complexity between the two states, and determine how well the metric
can distinguish between the two.
Potential avenues for PhD project:

1A
Diagnosis of central auditory processing disorder.
cochlear implant (CI) is a highly successful neurosensory prosthetic, able to restore some auditory function to the profoundly deaf. It is
composed of an array of electrodes implanted into the subject’s inner ear and an externally worn speech processing unit.


Informing cochlear implant speech processing strategies.
Diagnosis/monitoring of neurodegenerative/neurodevelopmental disorders.
References:
Bosl, W., Tierney, A., Tager-Flusberg, H. and Nelson, C. (2011), “EEG complexity as a biomarker for autism
spectrum disorder risk”, BMC Medicine, 9(18).
Cogan, G.B. and Poeppel, D. (2011), “A mutual information analysis of neural coding of speech by lowfrequency MEG phase information”, J. Neurophysiol., 106, pp554-563.
Costa, M., Goldberger, A.L., and Peng, C.-K.(2005), “Multiscale entropy analysis of biological signals”, Phys.
Rev. E, 71, pp 021906-1 – 021906-18.
Lippe, S.,Kovacevic, N. and McIntosh, A.R.,(2009), “Differentual maturation of brain signal complexity in the
human auditory and visual system”, Fontiers in human neuroscience, 3(48), p1-9.
Aiken, S.J. and Picton, T.W. (2008), “Human cortical responses to the speech envelope”, Ear and Hearing,
29(2), pp139-157.
Xie, H.-B., He, W.-X.and Liu, H.(2008), “Measuring time series regularity using nonlinear similarity-based
sample entropy”, Phys. Lett. A., 372, pp7140-7146.