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(searched for: doi:10.13176/11.370)
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2016 International Joint Conference on Neural Networks (IJCNN) pp 2090-2095; https://doi.org/10.1109/ijcnn.2016.7727457

Abstract:
Brain Computer Interface (BCI) system converts thoughts into commands for driving external device with Electroencephalography (EEG). This paper presents the use of decimation filters for filtering the EEG signal. Common Spatial Pattern (CSP) technique is used to transform the filtered signal to a new time series in order to have optimal variance for the discrimination of different tasks. Fishers Discriminant Analysis (FDA) is applied to the CSP features and the FDA scores are fed to a Support Vector Machine (SVM) classifier. The method is evaluated on BCI Competition III Dataset IVa and compared with other related state-of-the-art approaches. The results show that our method outperforms all other approaches in terms of average classification error rate. Compared to best performing method that uses only CSP features, the results obtained in this research offer on average a reduction of 1.07% in the classification error rate.
James Lyons, Kuldip K. Paliwal, Abdollah Dehzangi, Rhys Heffernan, ,
Published: 1 March 2016
Journal of Theoretical Biology, Volume 393, pp 67-74; https://doi.org/10.1016/j.jtbi.2015.12.018

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, Kuldip K. Paliwal
Published: 1 March 2015
Neurocomputing, Volume 151, pp 207-214; https://doi.org/10.1016/j.neucom.2014.09.051

Abstract:
The regularized linear discriminant analysis (RLDA) technique is one of the popular methods for dimensionality reduction used for small sample size problems. In this technique, regularization parameter is conventionally computed using a cross-validation procedure. In this paper, we propose a deterministic way of computing the regularization parameter in RLDA for small sample size problem. The computational cost of the proposed deterministic RLDA is significantly less than the cross-validation based RLDA technique. The deterministic RLDA technique is also compared with other popular techniques on a number of datasets and favorable results are obtained.Griffith Sciences, Griffith School of EngineeringFull Tex
James Lyons, Neela Biswas, , Abdollah Dehzangi, Kuldip K. Paliwal
Published: 1 August 2014
Journal of Theoretical Biology, Volume 354, pp 137-145; https://doi.org/10.1016/j.jtbi.2014.03.033

Abstract:
In protein fold recognition, a protein is classified into one of its folds. The recognition of a protein fold can be done by employing feature extraction methods to extract relevant information from protein sequences and then by using a classifier to accurately recognize novel protein sequences. In the past, several feature extraction methods have been developed but with limited recognition accuracy only. Protein sequences of varying lengths share the same fold and therefore they are very similar (in a fold) if aligned properly. To this, we develop an amino acid alignment method to extract important features from protein sequences by computing dissimilarity distances between proteins. This is done by measuring distance between two respective position specific scoring matrices of protein sequences which is used in a support vector machine framework. We demonstrated the effectiveness of the proposed method on several benchmark datasets. The method shows significant improvement in the fold recognition performance which is in the range of 4.3-7.6% compared to several other existing feature extraction methods.Griffith Sciences, Griffith School of EngineeringFull Tex
, Kuldip K. Paliwal
International Journal of Machine Learning and Cybernetics, Volume 6, pp 443-454; https://doi.org/10.1007/s13042-013-0226-9

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Jia-Ching Wang, Chang-Hong Lin, Bo-Wei Chen, Min-Kang Tsai
IEEE Transactions on Automation Science and Engineering, Volume 11, pp 607-613; https://doi.org/10.1109/tase.2013.2285131

Abstract:
This work presents a novel feature extraction approach called nonuniform scale-frequency map for environmental sound classification in home automation. For each audio frame, important atoms from the Gabor dictionary are selected by using the Matching Pursuit algorithm. After the system disregards phase and position information, the scale and frequency of the atoms are extracted to construct a scale-frequency map. Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are then applied to the scale-frequency map, subsequently generating the proposed feature. During the classification phase, a segment-level multiclass Support Vector Machine (SVM) is operated. Experiments on a 17-class sound database indicate that the proposed approach can achieve an accuracy rate of 86.21%. Furthermore, a comparison reveals that the proposed approach is superior to the other time-frequency methods.
, Kuldip K. Paliwal
Published: 1 July 2012
Pattern Recognition Letters, Volume 33, pp 1157-1162; https://doi.org/10.1016/j.patrec.2012.02.001

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