Use of graph metrics to classify motor imagery based BCI

The major aim of this work was to propose a novel method to perform a motor imagery (MI) based brain control interfacing system (BCI) classification using a single feature derived from the graph theory applied to connectivity measures. In particular, the characterization of small world coefficient is studied along different scenarios. Two connectivity measures as phase locking value (PLV) and coherence, two different frequency bands and two different time slots division (static and 3 different time windows). The second objective of this work was to study the viability of a novel stimuli for using on MI based BCIs, emotional schematic faces. Two emotions were showed to the participants: happiness and sadness to perform their MI tasks. Accuracy rates of up to 91.1% suggest that this is a promising strategy for BCI classifiers.

[1]  T.M. McGinnity,et al.  Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[3]  Dandan Huang,et al.  Towards a user-friendly brain–computer interface: Initial tests in ALS and PLS patients , 2010, Clinical Neurophysiology.

[4]  Stefan Haufe,et al.  Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.

[5]  Ramaswamy Palaniappan,et al.  Multiresolution analysis over graphs for a motor imagery based online BCI game , 2016, Comput. Biol. Medicine.

[6]  D. Simard,et al.  Fastest learning in small-world neural networks , 2004, physics/0402076.

[7]  Mahmoud Hassan,et al.  EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome , 2015, PloS one.

[8]  J. Martinerie,et al.  Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony , 2001, Journal of Neuroscience Methods.

[9]  B. Allison,et al.  The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[10]  Constantinos Gavriel,et al.  Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet wavelets & Common Spatial Pattern algorithms , 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

[11]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[12]  V Latora,et al.  Small-world behavior in time-varying graphs. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Chung-Hsien Kuo,et al.  Development of a motor imagery based brain-computer interface for humanoid robot control applications , 2016, 2016 IEEE International Conference on Industrial Technology (ICIT).

[14]  M. Peters,et al.  Volume conduction effects in EEG and MEG. , 1998, Electroencephalography and clinical neurophysiology.

[15]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[16]  A. Cichocki,et al.  A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.

[17]  Kup-Sze Choi,et al.  Improving the discrimination of hand motor imagery via virtual reality based visual guidance , 2016, Comput. Methods Programs Biomed..