Subject-Dependent and Subject-Independent Classification of Mental Arithmetic and Silent Reading Tasks

In this study, the electrical activities in the brain were classified during mental mathematical tasks and silent text reading. EEG recordings are collected from 18 healthy male university/college students, ages ranging from 18 to 25. During the study, a total of 60 slides including verbal text reading and arithmetical operations were presented to the subjects. EEG signals were collected from 26 channels in the course of slide show. Features were extracted by employing Hilbert Huang Transform (HHT). Then, subject-dependent and subject-independent classifications were performed using k-Nearest Neighbor (k-NN) algorithm with parameters k=1, 3, 5 and 10. Subject-dependent classifications resulted in accuracy rates between 95.8% and 99%, whereas the accuracy rates were between 92.2% and 97% for subject independent classification. The results show that EEG data recorded during mathematical and silent reading tasks can be classified with high accuracy results for both subject-dependent and subject-independent analysis.

[1]  Hung T. Nguyen,et al.  Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Jyh-Yeong Chang,et al.  Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors , 2012, Journal of NeuroEngineering and Rehabilitation.

[3]  Nasour Bagheri,et al.  Multiple classifier system for EEG signal classification with application to brain–computer interfaces , 2012, Neural Computing and Applications.

[4]  Xiaogang Ruan,et al.  Feature extraction of SSVEP-based brain-computer interface with ICA and HHT method , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[5]  Ram Bilas Pachori,et al.  Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals , 2013 .

[6]  Chin-Feng Lin,et al.  Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism , 2015, Journal of Medical Systems.

[7]  Luay Fraiwan,et al.  Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates , 2011, Journal of Medical Systems.

[8]  Esen Yildirim,et al.  Comparison of wavelets for classification of cognitive EEG signals , 2015, 2015 23nd Signal Processing and Communications Applications Conference (SIU).

[9]  Faiyaz Doctor,et al.  Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert-Huang Transform Applied to Depth of Anaesthesia , 2015, Entropy.

[10]  Chen Lin,et al.  The Nonlinear and nonstationary Properties in EEG Signals: Probing the Complex Fluctuations by Hilbert-Huang Transform , 2009, Adv. Data Sci. Adapt. Anal..

[11]  Esen Yildirim,et al.  Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers , 2014, Comput. Math. Methods Medicine.

[12]  Alexander A. Fingelkurts,et al.  Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges , 2005, Signal Process..

[13]  Marie Chavent,et al.  EEG classification for the detection of mental states , 2015, Appl. Soft Comput..

[14]  Gerwin Schalk,et al.  Brain–computer symbiosis , 2008, Journal of neural engineering.

[15]  G. Tononi,et al.  Direct Evidence for Wake-Related Increases and Sleep-Related Decreases in Synaptic Strength in Rodent Cortex , 2010, The Journal of Neuroscience.

[16]  Dongxu Qi,et al.  Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform , 2006 .

[17]  Gang Wang,et al.  A Fan Control System Base on Steady-State Visual Evoked Potential , 2016, 2016 International Symposium on Computer, Consumer and Control (IS3C).

[18]  Edwin Lughofer,et al.  Human–Machine Interaction Issues in Quality Control Based on Online Image Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  J.R. Wolpaw,et al.  BCI meeting 2005-workshop on signals and recording methods , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[21]  Edwin Lughofer,et al.  On-line elimination of local redundancies in evolving fuzzy systems , 2011, Evol. Syst..

[22]  Meng Hu,et al.  Classification of Normal and Hypoxia EEG Based on Hilbert Huang Transform , 2005, 2005 International Conference on Neural Networks and Brain.

[23]  Moncef Gabbouj,et al.  Epileptic Seizure Classification of EEG Time-Series Using Rational Discrete Short-Time Fourier Transform , 2015, IEEE Transactions on Biomedical Engineering.

[24]  B. Kannapiran,et al.  EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications , 2013, 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN).

[25]  Yang Wang,et al.  EEG-Based Real-Time Drowsiness Detection Using Hilbert-Huang Transform , 2015, 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[26]  Adel M. Alimi,et al.  Drowsy driver detection by EEG analysis using Fast Fourier Transform , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[27]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[28]  Xiaoli Li,et al.  Characterizing heat-sensitization responses in suspended moxibustion with high-density EEG. , 2014, Pain medicine.

[29]  N. Huang,et al.  The Mechanism for Frequency Downshift in Nonlinear Wave Evolution , 1996 .