2014
DOI: 10.1007/s11760-013-0600-9
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EEG signal analysis using spectral correlation function & GARCH model

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Cited by 12 publications
(6 citation statements)
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References 28 publications
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“…for only few cases, e.g., Acc. is 100 % for case 1 (A-E) and case 9 (C-E) in [10], for case 1 (A-E) in [11], and for Case 12 (ABCD-E) in [2]. The proposed method is computationally efficient, and time taken by CPU (2.93 GHz) in estimation of all features (a) for all the five sets of full length (23.6 s) data (10,000 features) is 13.27 s, (b) for two sets of data (4000 features) is 5.3 s and (c) for classification by LS-SVM (with tenfold cross-validation) is 0.106 s in MATLAB implementation.…”
Section: Simulation Results and Discussionmentioning
confidence: 96%
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“…for only few cases, e.g., Acc. is 100 % for case 1 (A-E) and case 9 (C-E) in [10], for case 1 (A-E) in [11], and for Case 12 (ABCD-E) in [2]. The proposed method is computationally efficient, and time taken by CPU (2.93 GHz) in estimation of all features (a) for all the five sets of full length (23.6 s) data (10,000 features) is 13.27 s, (b) for two sets of data (4000 features) is 5.3 s and (c) for classification by LS-SVM (with tenfold cross-validation) is 0.106 s in MATLAB implementation.…”
Section: Simulation Results and Discussionmentioning
confidence: 96%
“…where α and b are obtained from (11). In this work, we use following kernel functions: (a) linear kernel:…”
Section: Least-squares Support Vector Machinementioning
confidence: 99%
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“…Moreover, similar auto-regressive models appear in modelling computer network traffic data [11,83,84]. Beside this non-linear GARCH/ARCH models are recently used to analyse various data such as weather data [85], EEG signal analysis [86], brain activity analysis [87], EMG (electromyography) data analysis [88], speech signal analysis [89], and sonar imaging [90].…”
Section: Central Limit Theoremmentioning
confidence: 99%
“…We investigated this theory, and the findings support it. In this study, we investigated the cyclostationarity of the Slow Cortical Potentials (SCP) EEG signals, following our previous studies on ECG [74] and EEG [75] signals. Cyclostationary signals are continuous random signals that undergo periodic changes in their statistical features across time [76].…”
Section: Introductionmentioning
confidence: 99%