Mental Task Classification Based on HMM and BPNN

Effective feature extraction and accurate classification of EEG signals have important role in performance of Brain-Computer Interface (BCI) systems. In this paper, a mental task classification approach based on HMM and BPNN is proposed. In this approach, spectral and spatial features are extracted from the L-second epochs. Then transition matrix is calculated based on Hidden Markov Model (HMM) to reduce the feature vector dimension for each the extracted features sequence. Finally, a multi layer perceptron (MLP) neural network is used to classify and recognize the different mental task. The proposed approach is applied to classify three mental tasks (left hand movement imagination, right hand movement imagination and word generation) and it's performance has been evaluated for some influence parameters and other existing methods.

[1]  Wang Wei,et al.  Classifying EEG Signals Based HMM-AR , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[2]  G. Pfurtscheller,et al.  Continuous EEG classification during motor imagery-simulation of an asynchronous BCI , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Wei Wang,et al.  Clustering Linear Discriminant Analysis for MEG-Based Brain Computer Interfaces , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  J. Stastny,et al.  ssMRP state detection for brain computer interfacing using hidden Markov models , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[5]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[6]  Cheng-Jian Lin,et al.  Classification of mental task from EEG data using neural networks based on particle swarm optimization , 2009, Neurocomputing.

[7]  Sixto Ortiz.  Brain-computer interfaces: where human and machine meet , 2007, Computer.

[8]  Amit Konar,et al.  Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data , 2010, 2010 International Conference on Systems in Medicine and Biology.

[9]  M.B. Khalid,et al.  A Brain Computer Interface (BCI) using Fractional Fourier Transform with Time Domain normalization and heuristic weight adjustment , 2008, 2008 9th International Conference on Signal Processing.

[10]  Abbas Erfanian,et al.  An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  S.M. Hosni,et al.  Classification of EEG signals using different feature extraction techniques for mental-task BCI , 2007, 2007 International Conference on Computer Engineering & Systems.

[12]  Ping Yang,et al.  An Empirical Bayesian Framework for Brain–Computer Interfaces , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  Yong Li,et al.  Single trial EEG classification during finger movement task by using hidden Markov models , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[14]  N. Birbaumer,et al.  fMRI Brain-Computer Interfaces , 2008, IEEE Signal Processing Magazine.

[15]  Huosheng Hu,et al.  Adaptive schemes applied to online SVM for BCI data classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  R. Singla,et al.  Environment Control Using BCI , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[17]  José del R. Millán,et al.  Brain-Computer Interfaces , 2020, Handbook of Clinical Neurology.