An EEG-based brain-computer interface for dual task driving detection

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components.

[1]  Katja Kircher,et al.  Driver distraction : a review of the literature , 2007 .

[2]  Linda Lundström,et al.  The pupils and optical systems of gecko eyes. , 2009, Journal of vision.

[3]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[4]  Claudia J. Stanny,et al.  Effects of distraction and experience on situation awareness and simulated driving , 2007 .

[5]  Jeroen Breebaart,et al.  Deleted DOI: Multichannel Coding of Applause Signals , 2008 .

[6]  G. Wilson,et al.  Removal of ocular artifacts from electro-encephalogram by adaptive filtering , 2004, Medical and Biological Engineering and Computing.

[7]  Mervyn V. M. Yeo,et al.  Can SVM be used for automatic EEG detection of drowsiness during car driving , 2009 .

[8]  Babak Mahmoudi,et al.  Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface , 2005, Medical and Biological Engineering and Computing.

[9]  Matthias Rötting,et al.  Influence of Task Combination on EEG Spectrum Modulation for Driver Workload Estimation , 2011, Hum. Factors.

[10]  T. A. Kelley,et al.  Learning to attend: effects of practice on information selection. , 2009, Journal of vision.

[11]  Chin-Teng Lin,et al.  An EEG-Based Brain-Computer Interface for Dual Task Driving Detection , 2011, ICONIP.

[12]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[13]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[14]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[15]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[16]  Gian Luca Romani,et al.  Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis , 2007, NeuroImage.

[17]  Esa Alhoniemi,et al.  SOM Toolbox for Matlab 5 , 2000 .

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

[19]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[20]  H. Deubel,et al.  Saccade target selection and object recognition: Evidence for a common attentional mechanism , 1996, Vision Research.

[21]  Ricardo Nuno Vig.  Extraction of' ocular artefacts from EEG using independent component analysis , 1997 .

[22]  Ali Mansour,et al.  Blind Separation of Sources , 1999 .

[23]  T. Lagerlund,et al.  Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[26]  T.A. Larsen,et al.  Self-organizing map in recognition of topographic patterns of EEG spectra , 1995, IEEE Transactions on Biomedical Engineering.

[27]  Arthur F. Kramer,et al.  Neural correlates of dual-task performance after minimizing task-preparation , 2005, NeuroImage.

[28]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[29]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[30]  Chin-Teng Lin,et al.  Spatial and temporal EEG dynamics of dual-task driving performance , 2011, Journal of NeuroEngineering and Rehabilitation.

[31]  Farhad Faradji,et al.  Toward development of a two-state brain–computer interface based on mental tasks , 2011, Journal of neural engineering.

[32]  Tzyy-Ping Jung,et al.  EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach , 2008, EURASIP J. Adv. Signal Process..

[33]  V. Ibáñez,et al.  Frontal theta event-related synchronization: comparison of directed attention and working memory load effects , 2006, Journal of Neural Transmission.

[34]  Paolo Bonato,et al.  Patient specific ankle-foot orthoses using rapid prototyping , 2011, Journal of NeuroEngineering and Rehabilitation.

[35]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[36]  Mary M Hayhoe,et al.  Visual memory and motor planning in a natural task. , 2003, Journal of vision.

[37]  Kai-Quan Shen,et al.  EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate , 2008, Clinical Neurophysiology.

[38]  H. Asada,et al.  Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulate cortex in humans , 1999, Neuroscience Letters.

[39]  Chin-Teng Lin,et al.  Computational intelligent brain computer interaction and its applications on driving cognition , 2009, IEEE Computational Intelligence Magazine.

[40]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[41]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[42]  Panu Somervuo,et al.  Self-organizing maps of symbol strings , 1998, Neurocomputing.

[43]  A. Aron,et al.  Theta burst stimulation dissociates attention and action updating in human inferior frontal cortex , 2010, Proceedings of the National Academy of Sciences.