Sparse Linear Modeling of Speech from EEG
Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesisAlternative title
Gles Linjära Modellering av Tal från EEG (Swedish)
For people with hearing impairments, attending to a single speaker in a multi-talker background can be very difficult and something which the current hearing aids can barely help with. Recent studies have shown that the audio stream a human focuses on can be found among the surrounding audio streams, using EEG and linear models. With this rises the possibility of using EEG to unconsciously control future hearing aids such that the attuned sounds get enhanced, while the rest are damped. For such hearing aids to be useful for every day usage it better be using something other than a motion sensitive, precisely placed EEG cap. This could possibly be archived by placing the electrodes together with the hearing aid in the ear.
One of the leading hearing aid manufacturer Oticon and its research lab Erikholm Research Center have recorded an EEG data set of people listening to sentences and in which electrodes were placed in and closely around the ears. We have analyzed the data set by applying a range of signal processing approaches, mainly in the context of audio estimation from EEG. Two different types of linear sparse models based on L1-regularized least squares are formulated and evaluated, providing automatic dimensionality reduction in that they significantly reduce the number of channels needed. The first model is based on linear combinations of spectrograms and the second is based on linear temporal filtering. We have investigated the usefulness of the in-ear electrodes and found some positive indications. All models explored consider the in-ear electrodes to be the most important, or among the more important, of the 128 electrodes in the EEG cap.This could be a positive indication of the future possibility of using only electrodes in the ears for future hearing aids.
Place, publisher, year, edition, pages
2014. , 63 p.
EEG, In-Ear, Sparse, L1-regularization, Least Squares, FIR, Spectrogram, Machine Learning
IdentifiersURN: urn:nbn:se:liu:diva-108048ISRN: LiTH-ISY-EX-ET--14/0420--SEOAI: oai:DiVA.org:liu-108048DiVA: diva2:728869
Eriksholm Research Center, Oticon
Subject / course
2014-06-09, Algoritmen, Linköpings Universitet, Linköping, 22:50 (English)
Dahlin, Johan, lic.Borch Petersen, ElineLunner, Thomas, Adj. Prof.
Schön, Thomas, Prof.