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Assessing the (Sub)Conscious Processing of Noise based on ERPs

Anne Porbadnigk1, 2*, Jan-Niklas Antons2, 3, Benjamin Blankertz1, 2, 4, Matthias S. Treder1, Robert Schleicher2, 3, Sebastian Möller2, 3 and Gabriel Curio2, 5
  • 1 Berlin Institute of Technology, Machine Learning Laboratory, Germany
  • 2 Berlin Institute of Technology, Bernstein Focus: Neurotechnology, Germany
  • 3 Telekom Laboratories, Quality and Usabililty Lab, Germany
  • 4 Fraunhofer FIRST, Intelligent Data Analysis Group, Germany
  • 5 Charité, Department of Neurology and Clinical Neurophysiology, Germany

In the field of telecommunications, the quality of audio signals is typically assessed based on subjective behavioral data. However, some differences in quality might be too subtle to be detected consciously, while still possibly affecting the long term contentment of users. Neuro-physiological measures have the potential to complement behavioral approaches as objective and accurate measures for auditory quality assessment.
Here, we present results of an EEG study (N=11), in which we investigate the use of event-related potentials (ERPs) as such a quantitative measure. Subjects were presented with the phoneme /a/ that was either undisturbed (70% non-targets) or superimposed with four different degrees of signal-correlated noise.
The ERP analysis revealed the ’neuronal effort’ involved in detecting noise: the harder it is to detect noise in a stimulus, the higher the latency and the lower the amplitude of the P300 component. This is in line with related work, as the P300 latency is thought to index classification speed [1]. It is important to note that all of these trials are hits and thus indistinguishable on a behavioral level.
Remarkably, for two subjects, misses of marginally noisy targets result in an ERP pattern that shows a striking resemblance with hits (detected targets) of the same deviant class (see Fig.1). In order to assess the similarity, we employed a shrinkage LDA classifier [2] that was trained to distinguish between ERP patterns of hits and non-targets for a given subject. Notably, we found that the classifier was well able to also discriminate between misses and non-targets for these two subjects (see Fig.1) – even though the behavioral data suggests that the stimuli were perceived in the same way. We conjecture that the noise is missed on a conscious level during these trials, but still processed subconsciously.
We conclude that ERPs have the potential to be used successfully as a quantitative measure for the assessment of auditory quality, providing complementary information to conventional behavioral methods.

Fig. 1. T-scaled scalp plots showing similarities between hits and misses (subject Vpcad, deviant class T2, significance level at 2.33).

Figure 1
Image

Acknowledgements

This work was supported by the BMBF, FKZ 01GQ0850.

References

[1] Polich J: Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol, 118(10): 2128–48, 2007.
[2] Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR: Single-trial analysis and classification of ERP components - a tutorial. Neuroimage, in press, 2010.

Keywords: computational neuroscience

Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010.

Presentation Type: Presentation

Topic: Bernstein Conference on Computational Neuroscience

Citation: Porbadnigk A, Antons J, Blankertz B, Treder MS, Schleicher R, Möller S and Curio G (2010). Assessing the (Sub)Conscious Processing of Noise based on ERPs. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00137

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Received: 24 Sep 2010; Published Online: 24 Sep 2010.

* Correspondence: Dr. Anne Porbadnigk, Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany, anne.k.porbadnigk@tu-berlin.de