BCI oriented EEG analysis using log energy entropy of wavelet packets

Abstract Brain computer interfacing (BCI) is the practice of establishing a direct communication between a human and a computer by self-regulation of some electrical activity in the brain. Electroencephalography (EEG) based BCI is one of the most promising form of this application where a subject learns to control brain waves of type μ-rhythm (8–14 Hz) or β-rhythm (14–30 Hz) or P300 event related potential or slow cortical potentials (SCPs). Intents of the person is extracted using signal processing techniques. In this study self-regulation of SCPs is studied using wavelet packet analysis (WPA), rather than traditional time or frequency domain methods. WPA, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high frequency resolution than Wavelet analysis. WPA subimages are analyzed further by log energy entropy. These feature vectors are fed into a multilayer perceptron (MLP) for classification. We use the BCI Competition 2003 datasets Ia and Ib to test our approach. Performance of the MLP is compared with that of k-NN and SVM. Our method achieves accuracies of 92.8% and 63.3% on datasets Ia and Ib respectively. This outperforms the other reported results on the same datasets including the winners of the competition. Overall, WPA followed by Log Energy Entropy and MLP provides an efficient tool for the aforementioned kind of BCI oriented EEG analysis.

[1]  R. Lippmann.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[2]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[3]  Shiliang Sun,et al.  Assessing features for electroencephalographic signal categorization , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[4]  Kuo-Chung Lin,et al.  Wavelet packet feature extraction for vibration monitoring , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[5]  Jasmin Kevric,et al.  Biomedical Signal Processing and Control , 2016 .

[6]  César David Paredes Crovato,et al.  The Use of Wavelet Packet Transform and Artificial Neural Networks in Analysis and Classification of Dysphonic Voices , 2007, IEEE Transactions on Biomedical Engineering.

[7]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[8]  C. Neuper,et al.  Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..

[9]  Banghua Yang,et al.  Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces , 2016, Comput. Methods Programs Biomed..

[10]  Chong Zhang,et al.  Automatic recognition of cognitive fatigue from physiological indices by using wavelet packet transform and kernel learning algorithms , 2009, Expert Syst. Appl..

[11]  Mohammad Pooyan,et al.  An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine , 2012, Biomed. Signal Process. Control..

[12]  Guang Li,et al.  EEG Recognition Based on Multiple Types of Information by Using Wavelet Packet Transform and Neural Networks , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[13]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

[14]  Saeid Nahavandi,et al.  EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems , 2015, Expert Syst. Appl..

[15]  Justin Werfel,et al.  BCI competition 2003-data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals , 2004, IEEE Transactions on Biomedical Engineering.

[16]  Dario Farina,et al.  Single-trial discrimination of type and speed of wrist movements from EEG recordings , 2009, Clinical Neurophysiology.

[17]  Xi Chen,et al.  Feature extraction of motor imagery EEG signals based on wavelet packet decomposition , 2011, The 2011 IEEE/ICME International Conference on Complex Medical Engineering.

[18]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[19]  M. Victor Wickerhauser,et al.  Adapted wavelet analysis from theory to software , 1994 .

[20]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[21]  R. Haddad,et al.  Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets , 1992 .

[22]  M. W. Roth.  Survey of neural network technology for automatic target recognition , 1990, IEEE Trans. Neural Networks.

[23]  W. J. Wang,et al.  Application of orthogonal wavelets to early gear damage detection , 1995 .

[24]  Sazali Yaacob,et al.  Pathological infant cry analysis using wavelet packet transform and probabilistic neural network , 2011, Expert Syst. Appl..

[25]  Dario Farina,et al.  Offline Identification of Imagined Speed of Wrist Movements in Paralyzed ALS Patients from Single-Trial EEG , 2009, Front. Neuropro..

[26]  Onder Aydemir,et al.  A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data , 2010, Pattern Recognit. Lett..

[27]  Saeid Nahavandi,et al.  Motor Imagery Data Classification for BCI Application Using Wavelet Packet Feature Extraction , 2014, ICONIP.

[28]  K H Mauritz,et al.  Multimodal EEG analysis in man suggests impairment-specific changes in movement-related electric brain activity after stroke. , 2000, Brain : a journal of neurology.

[29]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[30]  Martin T. Hagan,et al.  Neural network design , 1995 .

[31]  J. Kalaska,et al.  Learning to Move Machines with the Mind , 2022 .

[32]  Ting Wu,et al.  Motor Imagery EEG Recognition Based on WPD-CSP and KF-SVM in Brain Computer Interfaces , 2014 .

[33]  N Sriraam,et al.  Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier , 2016, Cognitive Neurodynamics.