Title

Classification of EEG Signals Using Adaptive Time-Frequency Distributions

Journal title

Metrology and Measurement Systems

Yearbook

2016

Volume

vol. 23

Issue

No 2

Authors

Khan, Nabeel A. ; Ali, Sadiq

Keywords

Adaptive Directional Time-Frequency Distribution ; EEG signals ; Time-Frequency features ; pattern recognition

Divisions of PAS

Nauki Techniczne

Coverage

251-260

Publisher

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Date

2016.06.30

Type

Artykuły / Articles

Identifier

DOI: 10.1515/mms-2016-0021 ; ISSN 2080-9050, e-ISSN 2300-1941

Source

Metrology and Measurement Systems; 2016; vol. 23; No 2; 251-260

References

Boashash (1992), Estimating and interpreting the instantaneous frequency of a signal Fundamentals of the, Proc IEEE, 80, 520, doi.org/10.1109/5.135376 ; Sharma (2015), Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions with, Expert Systems Applications, 42, 1106, doi.org/10.1016/j.eswa.2014.08.030 ; Sharma (2015), Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals, Entropy, 17, 669, doi.org/10.3390/e17020669 ; Sameh (2012), Multiclass Support Vector Machines for Environmental Sounds Classification Using log Gabor Filters of Engineering and Technology, World Academy Science, 6, 1185. ; Khan (2015), Multi - component instantaeous frequency estimation using locally adaptive directional time frequency distributions of Adaptive Control and Signal Processing, International Journal, doi.org/10.1002/acs.2583 ; Joshi (2014), Classification of ictal and seizure - free EEG signals using fractional linear prediction Processing and, Biomedical Signal Control, 9, 1, doi.org/10.1016/j.bspc.2013.08.006 ; Wang (2015), Time - Frequency Feature Representation Using Multi - Resolution Texture Analysis and Acoustic Activity Detector for Real - Life Speech Emotion Recognition, Sensors, 15, 1458, doi.org/10.3390/s150101458 ; Bastiaans (2002), On Rotated Time - Frequency Kernels Processing, IEEE Signal Letters, 9, 378, doi.org/10.1109/LSP.2002.805118 ; Boashash (2014), A review of time - frequency matched filter design with application to seizure detection in multichannel newborn EEG, Digital Signal Processing, 28, 28, doi.org/10.1016/j.dsp.2014.02.007 ; Fu (2015), Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals Processing and, Biomedical Signal Control, 18, 179, doi.org/10.1016/j.bspc.2015.01.002 ; Abdulla (2011), Neonatal EEG signal characteristics using time frequency analysis A : Statistical Mechanics and its, Physica Applications, 390, 1096, doi.org/10.1016/j.physa.2010.11.013 ; Auger (2013), Timefrequency reassignment and synchrosqueezing : An overview Processing, IEEE Signal Magazine, 30, 32, doi.org/10.1109/MSP.2013.2265316 ; Boashash (2015), Principles of time - frequency feature extraction for change detection in non - stationary signals : Application to newborn EEG abnormalitiy detection, Pattern Recognition, 48, 616, doi.org/10.1016/j.patcog.2014.08.016 ; Peng (2005), Feature selection based on mutual information criteria of maxdependency , max - relevance , and min - redundancy on Pattern Analysis and Machine, IEEE Transactions Intelligence, 27, 1226. ; Jacob (2004), Design of steerable filters for feature detection using canny - lie criteria on Pattern Analysis and Machine, IEEE Transactions Intelligence, 26, 1007. ; Boashash (2015), Time - frequency features for pattern recognition using high resolution TFDs A tutorial review, Digital Signal Processing, 40, 1, doi.org/10.1016/j.dsp.2014.12.015