Facial expressions in EEG/EMG recordings

Boot, L. (2009) Facial expressions in EEG/EMG recordings.

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Abstract:With focus on natural intuitive interaction of humans with their media, new ways of interacting are being studied. Brain computer interface (BCI), originally focussed on people with disabilities, is a relative new field for providing natural interactivity between a user and a computer. Using scalp EEG caps, healthy consumers can potentially use BCI applications for daily entertaining purposes, for example gaming. Using facial expressions on the other hand is one of the most natural ways of non-verbal communication. At the moment, there are several different techniques for a computer to read facial expressions. EEG recording is one of them that is hardly or not at all studied at the present, but would make an interesting addition for commercial BCI devices. Because actual consumers are believed to be only interested in how well a device works, rather than how it works, it was decided to also look at EMG signals visible in recordings done with an EEG recording device. Thus the topic of this research is facial expressions in recordings from a scalp EEG device, rather than facial expressions in BCI. It was expected that EMG signals, visible in recorded EEG data, are bigger than the EEG signals them self. The goals of this study were to gather EEG and EMG data, recorded with an EEG device, of voluntary facial expressions, and to analyze it. The hypothesis tested in this study was: facial expression can be classified with an accuracy over 70% in an EEG recording. Sub-hypotheses de�ned were: EMG influence on the classification accuracy is significant larger that EEG in uence, frontal electrodes will not yield significantly lower classification accuracies compared to using all 32 electrodes and using facial expressions with partially overlapping muscles will yield significantly lower classification accuracies.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Human Media Interaction MSc (60030)
Link to this item:http://purl.utwente.nl/essays/58633
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