Use of phase-locking value in sensorimotor rhythm-based brain–computer interface: zero-phase coupling and effects of spatial filters

Phase-locking value (PLV) is a potentially useful feature in sensorimotor rhythm-based brain–computer interface (BCI). However, volume conduction may cause spurious zero-phase coupling between two EEG signals and it is not clear whether PLV effects are independent of spectral amplitude. Volume conduction might be reduced by spatial filtering, but it is uncertain what impact this might have on PLV. Therefore, the goal of this study was to explore whether zero-phase PLV is meaningful and how it is affected by spatial filtering. Both amplitude and PLV feature were extracted in the frequency band of 10–15 Hz by classical methods using archival EEG data of 18 subjects trained on a two-target BCI task. The results show that with right ear-referenced data, there is meaningful long-range zero-phase synchronization likely involving the primary motor area and the supplementary motor area that cannot be explained by volume conduction. Another novel finding is that the large Laplacian spatial filter enhances the amplitude feature but eliminates most of the phase information seen in ear-referenced data. A bipolar channel using phase-coupled areas also includes both phase and amplitude information and has a significant practical advantage since fewer channels required.

[1]  Xiaorong Gao,et al.  Bipolar electrode selection for a motor imagery based brain–computer interface , 2008, Journal of neural engineering.

[2]  Dean J. Krusienski,et al.  Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain–computer interface , 2012, Brain Research Bulletin.

[3]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[4]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[5]  G. Pfurtscheller,et al.  Dependence of coherence measurements on EEG derivation type , 1996, Medical and Biological Engineering and Computing.

[6]  C. Tenke,et al.  Surface Laplacians (SL) and phase properties of EEG rhythms: Simulated generators in a volume-conduction model. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  D. Halliday,et al.  Volume conduction effects in brain network inference from electroencephalographic recordings using phase lag index , 2012, Journal of Neuroscience Methods.

[9]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[10]  Olaf Sporns,et al.  Mechanisms of Zero-Lag Synchronization in Cortical Motifs , 2013, PLoS Comput. Biol..

[11]  E. V. Simpson,et al.  Evaluation of an automatic cardiac activation detector for bipolar electrograms , 1993, Medical and Biological Engineering and Computing.

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

[13]  Kristina Moll,et al.  Letter-sound processing deficits in children with developmental dyslexia: An ERP study , 2016, Clinical Neurophysiology.

[14]  Xiaorong Gao,et al.  Design of electrode layout for motor imagery based brain--computer interface , 2007 .

[15]  Clemens Brunner,et al.  Online Control of a Brain-Computer Interface Using Phase Synchronization , 2006, IEEE Transactions on Biomedical Engineering.

[16]  Stuart N Baker,et al.  Cells in somatosensory areas show synchrony with beta oscillations in monkey motor cortex , 2007, The European journal of neuroscience.

[17]  Dennis J. McFarland,et al.  Design and operation of an EEG-based brain-computer interface with digital signal processing technology , 1997 .

[18]  D. Tucker,et al.  EEG coherency II: experimental comparisons of multiple measures , 1999, Clinical Neurophysiology.

[19]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[20]  Dennis J McFarland,et al.  The advantages of the surface Laplacian in brain-computer interface research. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[21]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[22]  M. Hallett,et al.  Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements. , 1997, Electroencephalography and clinical neurophysiology.

[23]  Yijun Wang,et al.  Amplitude and phase coupling measures for feature extraction in an EEG-based brain–computer interface , 2007, Journal of neural engineering.

[24]  G. Pfurtscheller,et al.  On the existence of different alpha band rhythms in the hand area of man , 1997, Neuroscience Letters.

[25]  Ole Jensen,et al.  Posterior alpha oscillations reflect attentional problems in boys with Attention Deficit Hyperactivity Disorder , 2016, Clinical Neurophysiology.

[26]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[27]  Dean J. Krusienski,et al.  A Method for Visualizing Independent Spatio-Temporal Patterns of Brain Activity , 2009, EURASIP J. Adv. Signal Process..

[28]  G Pfurtscheller,et al.  Event-related coherence as a tool for studying dynamic interaction of brain regions. , 1996, Electroencephalography and clinical neurophysiology.

[29]  Pascal Fries,et al.  Communication through coherence with inter-areal delays , 2015, Current Opinion in Neurobiology.

[30]  A. C. Papanicolaou,et al.  Modular Patterns of Phase Desynchronization Networks During a Simple Visuomotor Task , 2015, Brain Topography.

[31]  Karl J. Friston,et al.  Zero-lag synchronous dynamics in triplets of interconnected cortical areas , 2001, Neural Networks.

[32]  G. Pfurtscheller,et al.  Do changes in coherence always reflect changes in functional coupling? , 1998, Electroencephalography and clinical neurophysiology.

[33]  Dennis J. McFarland,et al.  Brain-computer interface (BCI) operation: signal and noise during early training sessions , 2005, Clinical Neurophysiology.

[34]  Jonathan R Wolpaw,et al.  EEG-Based Communication and Control: Speed–Accuracy Relationships , 2003, Applied psychophysiology and biofeedback.

[35]  Dennis J. McFarland,et al.  Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans , 2003, Neuroscience Letters.

[36]  Patrick Celka,et al.  Statistical Analysis of the Phase-Locking Value , 2007, IEEE Signal Processing Letters.

[37]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[38]  Bernadette C. M. van Wijk,et al.  On the Influence of Amplitude on the Connectivity between Phases , 2011, Front. Neuroinform..

[39]  Yijun Wang,et al.  Phase Synchrony Measurement in Motor Cortex for Classifying Single-trial EEG during Motor Imagery , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  G. Pfurtscheller,et al.  Calculation of event-related coherence—A new method to study short-lasting coupling between brain areas , 2005, Brain Topography.

[41]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[42]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[43]  C. Tenke,et al.  Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions , 2012, Clinical Neurophysiology.

[44]  Hongzhi Qi,et al.  A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action , 2010, Journal of neural engineering.

[45]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[46]  Louis A. Schmidt,et al.  Regional electroencephalogram (EEG) spectral power and hemispheric coherence in young adults born at extremely low birth weight , 2009, Clinical Neurophysiology.

[47]  Gabriel Curio,et al.  It is not all about phase: Amplitude dynamics in corticomuscular interactions , 2013, NeuroImage.

[48]  Bernhard Graimann,et al.  Phase coupling between different motor areas during tongue-movement imagery , 2004, Neuroscience Letters.