JCSE, vol. 7, no. 2, pp.112-121, 2013

DOI: http://dx.doi.org/10.5626/JCSE.2013.7.2.112

Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning

Anne K. Porbadnigk, Nico Gornitz, Marius Kloft, Klaus-Robert Muller
Machine Learning Laboratory, Berlin Institute of Technology, Berlin, and DFG Research Training Group ??밻nsory Computation in Neural Systems?? GRK 1589/1, Berlin, Germany/ Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany/ Courant Institute of Mathematical Sciences, New York, and Memorial Sloan-Kettering Cancer Center, New York, NY, USA/ Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany

Abstract: The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.

Keyword: Brain-computer interfacing; EEG; Semi-supervised learning; Systematic label noise; Anomaly detection

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