Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

  1. Microstates as Disease and Progression Markers in Patients With Mild Cognitive Impairment

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. An in vivo Mouse Model to Investigate the Effect of Local Anesthetic Nanomedicines on Axonal Conduction and Excitability

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Computing Generalized Matrix Inverse on Spiking Neural Substrate

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Deep Learning Based Attenuation Correction of PET/MRI in Pediatric Brain Tumor Patients: Evaluation in a Clinical Setting

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Multiway canonical correlation analysis of brain data

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Decoding the auditory brain with canonical component analysis

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Task-Modulated Cortical Representations of Natural Sound Source Categories

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Subcortical and cortical correlates of pitch discrimination: Evidence for two levels of neuroplasticity in musicians

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  5. Representation of pitch across spectral regions in human auditory cortex

    Publikation: KonferencebidragKonferenceabstrakt til konferenceForskningpeer review

  • Daniel D E Wong
  • Søren A Fuglsang
  • Jens Hjortkjær
  • Enea Ceolini
  • Malcolm Slaney
  • Alain de Cheveigné
Vis graf over relationer

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies.

OriginalsprogEngelsk
TidsskriftFrontiers in Neuroscience
Vol/bind12
Udgave nummer531
ISSN1662-4548
DOI
StatusUdgivet - 7 aug. 2018

ID: 55065067