Use of data driven optimal filter to obtain significant trend present in frequency domain parameters for scalp EEG captured during meditation

Chaudhuri, Aritra; Nayak, Siddharth; Routray, Aurobinda

doi:10.1109/techsym.2014.6807905

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The first three rows of Table II represent the performances of the same while working with different kernel functions e.g. Gaussian radial basis (rbf), linear, multilayer perceptron (mlp) respectively [19]. Gaussian rbf kernel outperforms others obtaining maximum g-metric means.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, normalization, relevant attribute selections have been carried out to improve the performance of the classification framework in [9]. In another domain [19], [20], band pass filtering has been used to remove artifacts from raw EEG data followed by normalization in pre-processing stage. The selection of appropriate parameters of the pre-processing techniques are dependent on the user.…”

Section: Introductionmentioning

confidence: 99%

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Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS

Chaki

Routray

Mohanty

et al. 2015

2015 Annual IEEE India Conference (INDICON)

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Abstract-This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve prediction problem. The proposed two-stage Domain Knowledge based Fuzzy Information System (DKFIS) improves the prediction accuracy attained by ANFIS alone. The proposed framework has been implemented on a noisy and incomplete dataset acquired from a hydrocarbon field located at western part of India. Here, oil saturation has been predicted from four different well logs i.e. gamma ray, resistivity, density, and clay volume. In the first stage, depending on zero or near zero and non-zero oil saturation levels the input vector is classified into two classes (Class 0 and Class 1) using SVM. The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i.e. well logs and target variable -oil saturation. Second, an ANFIS is designed to predict non-zero (Class 1) oil saturation values from predictor logs. The predicted output has been further refined based on expert knowledge. It is apparent from the experimental results that the expert intervention with qualitative judgment at each stage has rendered the prediction into the feasible and realistic ranges. The performance analysis of the prediction in terms of four performance metrics such as correlation coefficient (CC), root mean square error (RMSE), and absolute error mean (AEM), scatter index (SI) has established DKFIS as a useful tool for reservoir characterization.

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Section: Resultsmentioning

confidence: 99%