Clustering based Approach for Automated EEG Artifacts Handling

Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%.

[1]  M van de Velde,et al.  Detection of muscle artefact in the normal human awake EEG. , 1998, Electroencephalography and clinical neurophysiology.

[2]  Tele Tan,et al.  Classifying eye and head movement artifacts in EEG signals , 2011, 5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011).

[3]  Bashir I. Morshed,et al.  Sample Entropy enhanced wavelet-ICA denoising technique for eye blink artifact removal from scalp EEG dataset , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[4]  Saeid Sanei,et al.  Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm , 2005, IEEE Signal Processing Letters.

[5]  Geoffrey E. Hinton,et al.  Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates , 2000, J. VLSI Signal Process..

[6]  Onder Aydemir,et al.  Classification of various facial movement artifacts in EEG signals , 2012, 2012 35th International Conference on Telecommunications and Signal Processing (TSP).

[7]  M. Tangermann,et al.  Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals , 2011, Behavioral and Brain Functions.

[8]  Roozbeh Jafari,et al.  Automatic EEG artifact removal based on ICA and Hierarchical Clustering , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  G. Gratton.  Dealing with artifacts: The EOG contamination of the event-related brain potential , 1998 .

[10]  R. B. Reilly,et al.  FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection , 2010, Journal of Neuroscience Methods.

[11]  Mohammad Ali Badamchizadeh,et al.  Artifacts removal in EEG signal using a new neural network enhanced adaptive filter , 2013, Neurocomputing.

[12]  Donald W. Klass,et al.  The Continuing Challenge of Artifacts in the EEG , 1995 .

[13]  W. Klonowski.  Everything you wanted to ask about EEG but were afraid to get the right answer , 2009, Nonlinear biomedical physics.

[14]  Michael Q. Zhang,et al.  EVALUATION AND COMPARISON OF CLUSTERING ALGORITHMS IN ANGLYZING ES CELL GENE EXPRESSION DATA , 2003 .

[15]  Vera Kaiser,et al.  What does clean EEG look like? , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Onder Aydemir,et al.  Classifying Various EMG and EOG Artifacts in EEG Signals , 2012 .

[17]  D. Migotina,et al.  Automatic Artifacts Detection and Classification in Sleep EEG Signals Using Descriptive Statistics and Histogram Analysis: Comparison of Two Detectors , 2012, 2012 Spring Congress on Engineering and Technology.

[18]  Jiang Li,et al.  EOG artifact removal using a wavelet neural network , 2012, Neurocomputing.

[19]  George Zouridakis,et al.  A hybrid algorithm for artifact rejection in EEG recordings based on iterative ICA and fuzzy clustering , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Tran Duc-Tan,et al.  An effective procedure for reducing EOG and EMG artefacts from EEG signals , 2013, 2013 International Conference on Advanced Technologies for Communications (ATC 2013).

[21]  Michael Q. Zhang,et al.  Evaluation and comparison of clustering algorithms in analyzing es cell gene expression data , 2002 .

[22]  Junshui Ma,et al.  Muscle artifacts in multichannel EEG: Characteristics and reduction , 2012, Clinical Neurophysiology.

[23]  Durk Talsma,et al.  Auto-adaptive averaging: detecting artifacts in event-related potential data using a fully automated procedure. , 2008, Psychophysiology.

[24]  R. Ranta,et al.  EEG Ocular Artefacts and Noise Removal , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Reinhold Scherer,et al.  FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  Slawomir J. Nasuto,et al.  Automatic Artefact Removal from Event-related Potentials via Clustering , 2007, J. VLSI Signal Process..

[27]  Abbas Erfanian,et al.  A fully automatic ocular artifact suppression from EEG data using higher order statistics: improved performance by wavelet analysis. , 2010, Medical engineering & physics.