|
Título:
|
EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts
|
|
Autor/a:
|
Vialatte, François B.; Solé-Casals, Jordi; Cichocki, Andrej
|
|
Otros autores:
|
Universitat de Vic. Grup de Recerca en Tecnologies Digitals; Universitat de Vic. Escola Politècnica Superior |
|
Notas:
|
EEG recordings are usually corrupted by spurious extra-cerebral artifacts,
which should be rejected or cleaned up by the practitioner. Since manual
screening of human EEGs is inherently error prone and might induce
experimental bias, automatic artifact detection is an issue of importance.
Automatic artifact detection is the best guarantee for objective and clean results.
We present a new approach, based on the time–frequency shape of muscular
artifacts, to achieve reliable and automatic scoring. The impact of muscular
activity on the signal can be evaluated using this methodology by placing
emphasis on the analysis of EEG activity. The method is used to discriminate
evoked potentials from several types of recorded muscular artifacts—with a
sensitivity of 98.8% and a specificity of 92.2%. Automatic cleaning ofEEGdata
are then successfully realized using this method, combined with independent
component analysis. The outcome of the automatic cleaning is then compared
with the Slepian multitaper spectrum based technique introduced by Delorme
et al (2007 Neuroimage 34 1443–9). |
|
Materia(s):
|
-Tractament del senyal |
|
Derechos:
|
© Institute of Physics (IOP). Published article can be found at http://iopscience.iop.org/0967-3334/29/12/007
|
|
Tipo de documento:
|
Artículo
info:eu-repo/acceptedVersion |
|
Editor:
|
Institute of Physics
|
|
Compartir:
|
|
