Discontinuous Adventitious Sounds Imaging by Semiautomatic Selection of Independent Components

2015 ◽  
pp. 631-634
Author(s):  
S. Charleston-Villalobos ◽  
N. Castañeda-Villa ◽  
R. González-Camarena ◽  
M. Mejía-Ávila ◽  
T. Aljama-Corrales
Keyword(s):  
Independent Components ◽  
Selection Of

scholarly journals A Hybrid Algorithm for Predicting Median-Plane Head-Related Transfer Functions from Anthropometric Measurements

2019 ◽  
Vol 9 (11) ◽  
pp. 2323
Author(s):  
Xuejie Liu ◽  
Hao Song ◽  
Xiaoli Zhong
Keyword(s):  
Hybrid Algorithm ◽  
Transfer Functions ◽  
Spectral Characteristics ◽  
Median Plane ◽  
Objective Evaluation ◽  
Spectral Distortion ◽  
Anthropometric Parameters ◽  
Independent Components ◽  
Weight Coefficients ◽  
Selection Of

Since head-related transfer functions (HRTFs) represent the interactions between sounds and physiological structures of listeners, anthropometric parameters represent a straightforward way to customize (or predict) individualized HRTFs. This paper proposes a hybrid algorithm for predicting median-plane individualized HRTFs using anthropometric parameters. The proposed hybrid algorithm consists of three parts: decomposition of HRTFs; selection of key anthropometric parameters; and establishing a prediction formula. Firstly, an independent component analysis (ICA) is applied to median-plane HRTFs from multiple subjects to obtain independent components and subject-dependent weight coefficients. Then, a factor analysis is used to select key anthropometric parameters relevant to HRTFs. Finally, a regression formula that connects ICA weight coefficients to key anthropometric parameters is established by a multiple linear regression. Further, the effectiveness of the proposed hybrid algorithm is verified by an objective evaluation via spectral distortion and a subjective localization experiment. The results show that, when compared with generic Knowles Electronics Manikin for Acoustic Research (KEMAR) HRTFs, the spectral characteristics of the predicted HRTFs are closer to those of the individualized HRTFs. Moreover, the predicted HRTFs can alleviate front–back and up–down confusion and improve the accuracy of localization for most subjects.

scholarly journals Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Naeem ◽  
Clemens Brunner ◽  
Gert Pfurtscheller
Keyword(s):  
Motor Imagery ◽  
Visual Inspection ◽  
Full Range ◽  
Principal Component ◽  
Spatial Filters ◽  
Independent Components ◽  
Components Analysis ◽  
The Brain ◽  
Variance Criterion ◽  
Selection Of

The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.

Selection of independent components based on cortical mapping of electromagnetic activity

2012 ◽  
Vol 9 (5) ◽  
pp. 056006 ◽  
Author(s):  
Hui-Ling Chan ◽  
Yong-Sheng Chen ◽  
Li-Fen Chen
Keyword(s):  
Cortical Mapping ◽  
Independent Components ◽  
Selection Of
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