Sparse Linear Modeling of Speech from EEG

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...

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Bibliographic Details
Main Author: Tiger, Mattias
Format: Others
Language:English
Published: Linköpings universitet, Reglerteknik 2014
Subjects:
EEG
FIR
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108048
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spelling ndltd-UPSALLA1-oai-DiVA.org-liu-1080482016-05-05T05:12:28ZSparse Linear Modeling of Speech from EEGengGles Linjära Modellering av Tal från EEGTiger, MattiasLinköpings universitet, ReglerteknikLinköpings universitet, Tekniska högskolan2014EEGIn-EarSparseL1-regularizationLeast SquaresFIRSpectrogramMachine LearningFor 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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108048application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic EEG
In-Ear
Sparse
L1-regularization
Least Squares
FIR
Spectrogram
Machine Learning
spellingShingle EEG
In-Ear
Sparse
L1-regularization
Least Squares
FIR
Spectrogram
Machine Learning
Tiger, Mattias
Sparse Linear Modeling of Speech from EEG
description 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.
author Tiger, Mattias
author_facet Tiger, Mattias
author_sort Tiger, Mattias
title Sparse Linear Modeling of Speech from EEG
title_short Sparse Linear Modeling of Speech from EEG
title_full Sparse Linear Modeling of Speech from EEG
title_fullStr Sparse Linear Modeling of Speech from EEG
title_full_unstemmed Sparse Linear Modeling of Speech from EEG
title_sort sparse linear modeling of speech from eeg
publisher Linköpings universitet, Reglerteknik
publishDate 2014
url http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108048
work_keys_str_mv AT tigermattias sparselinearmodelingofspeechfromeeg
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