TY - CONF AV - none N1 - cited By 12; Conference of 2014 IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2014 ; Conference Date: 25 November 2014; Conference Code:112417 TI - Discriminating the different human brain states with EEG signals using Fractal dimension: A nonlinear approach Y1 - 2015/// KW - Biomedical signal processing; Brain; Brain computer interface; Decision making; Electroencephalography; Extraction; Feature extraction; Lyapunov methods; Pattern matching KW - Approximate entropy; Biological science; Brain-computer interfacing; Correlation dimensions; Lyapunov exponent; Modern applications; Nonlinear approach; Nonlinear features KW - Fractal dimension PB - Institute of Electrical and Electronics Engineers Inc. A1 - Ahmad, R.F. A1 - Malik, A.S. A1 - Kamel, N. A1 - Amin, H. A1 - Zafar, R. A1 - Qayyum, A. A1 - Reza, F. SN - 9781479980413 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988231419&doi=10.1109%2fICSIMA.2014.7047426&partnerID=40&md5=3dd6e2b8cf79836c6e67bfae6428cb79 N2 - EEG signals are measured on scalp of the human brain and are widely used to address the clinical as well as in modern application like brain computer interfacing (BCI) and gaming. Feature extraction plays a fundamental role for good classification purposes. EEG features commonly extracted are linear as well as nonlinear. Nonlinear approaches are used when the complexity of EEG signals increases. Nonlinear features like correlation dimension (CD), Lyapunov exponents, approximate entropy requires higher computational complexity. On other hand Fractal dimension (FD) requires less computations. Therefore, Fractal dimension are widely used in engineering and biological sciences. In our paper, Fractal dimension has been selected to discriminate the different brain states. EEG data from 08 healthy participants have been acquired during eyes open, eyes close and during IQ task. Fractal dimensions have been computed on the EEG data acquired. Using Fractal dimension, we have successfully discriminated the different brain conditions/states like eyes open, eyes close and IQ task. Results have shown better discrimination between mental task and active brain conditions with 91.66 accuracy using SVM classifier as compared to other classifiers. This approach can be used for fast decision making and pattern matching based on the selected epoch of the EEG signal using nonlinear approach. © 2014 IEEE. ID - scholars6014 ER -