Doubling the Speed of N200 Speller via Dual-Directional Motion Encoding

Objective: Motion-onset visual evoked potentials (mVEPs)-based spellers, also known as N200 spellers, have been successfully implemented, avoiding flashing stimuli that are common in visual brain-computer interface (BCI). However, their information transfer rates (ITRs), typically below 50 bits/min, are lower than other visual BCI spellers. In this study, we sought to improve the speed of N200 speller to a level above the well-known P300 spellers. Approach: Based on our finding of the spatio-temporal asymmetry of N200 response elicited by leftward and rightward visual motion, a novel dual-directional N200 speller was implemented. By presenting visual stimuli moving in two different directions simultaneously, the new paradigm reduced the stimuli presentation time by half, while ensuring separable N200 features between two visual motion directions. Furthermore, a probability-based dynamic stopping algorithm was also proposed to shorten the decision time for each output further. Both offline and online tests were conducted to evaluate the performance in ten participants. Main results: Offline results revealed contralateral dominant temporal and spatial patterns in N200 responses when subjects attended to stimuli moving leftward or rightward. In online experiments, the dual-directional paradigm achieved an average ITR of 79.8 bits/min, with the highest ITR of 124.8 bits/min. Compared with the traditional uni-directional N200 speller, the median gain on the ITR was 202%. Significance: The proposed dual-directional paradigm managed to double the speed of the N200 speller. Together with its non-flashing characteristics, this dual-directional N200 speller is promising to be a competent candidate for fast and reliable BCI applications.

[1]  Shangkai Gao,et al.  An online brain–computer interface using non-flashing visual evoked potentials , 2010, Journal of neural engineering.

[2]  Benjamin Blankertz,et al.  Exploring motion VEPs for gaze-independent communication , 2012, Journal of neural engineering.

[3]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[4]  Shangkai Gao,et al.  An N200 speller integrating the spatial profile for the detection of the non-control state , 2012, Journal of neural engineering.

[5]  Bo Hong,et al.  Bi-directional Visual Motion Based BCI Speller , 2019, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER).

[6]  Bo Hong,et al.  A Single-Stimulus, Multitarget BCI Based on Retinotopic Mapping of Motion-Onset VEPs , 2019, IEEE Transactions on Biomedical Engineering.

[7]  Hao Yang,et al.  The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential , 2017, Journal of neural engineering.

[8]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[9]  Jonathan W. Peirce,et al.  PsychoPy—Psychophysics software in Python , 2007, Journal of Neuroscience Methods.

[10]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[11]  Dongyang Li,et al.  Minimally invasive brain computer interface for fast typing , 2017, 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER).

[12]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[13]  G Townsend,et al.  Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain. , 2016, Journal of neural engineering.

[14]  A. Kübler,et al.  Flashing characters with famous faces improves ERP-based brain–computer interface performance , 2011, Journal of neural engineering.

[15]  Yijun Wang,et al.  Towards a fully spatially coded brain-computer interface: simultaneous decoding of visual eccentricity and direction , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Xingyu Wang,et al.  A combined brain–computer interface based on P300 potentials and motion-onset visual evoked potentials , 2012, Journal of Neuroscience Methods.

[17]  Xiaorong Gao,et al.  A brain–computer interface using motion-onset visual evoked potential , 2008, Journal of neural engineering.

[18]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[19]  Stefan Haufe,et al.  Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. , 2013, Journal of neural engineering.

[20]  Tao Liu,et al.  N200-speller using motion-onset visual response , 2009, Clinical Neurophysiology.

[21]  Wei Wu,et al.  A Novel Algorithm for Learning Sparse Spatio-Spectral Patterns for Event-Related Potentials , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[23]  Michael Bach,et al.  Directional tuning of human motion adaptation as reflected by the motion VEP , 2001, Vision Research.

[24]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[25]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[26]  Yang Liu,et al.  Comparison of stimulus types in visual P300 speller of brain-computer interfaces , 2010, 9th IEEE International Conference on Cognitive Informatics (ICCI'10).

[27]  Rui Zhang,et al.  An Adaptive Motion-Onset VEP-Based Brain-Computer Interface , 2015, IEEE Transactions on Autonomous Mental Development.

[28]  Rui Xu,et al.  Toward a minimally invasive brain–computer interface using a single subdural channel: A visual speller study , 2013, NeuroImage.

[29]  A. Cichocki,et al.  The Changing Face of P300 BCIs: A Comparison of Stimulus Changes in a P300 BCI Involving Faces, Emotion, and Movement , 2012, PloS one.

[30]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[31]  R. Andersen,et al.  Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[33]  Jane E. Raymond,et al.  Directional anisotropy of motion sensitivity across the visual field , 1994, Vision Research.

[34]  Yu Ji,et al.  A submatrix-based P300 brain-computer interface stimulus presentation paradigm , 2012, Journal of Zhejiang University SCIENCE C.

[35]  F. Tong,et al.  Decoding Seen and Attended Motion Directions from Activity in the Human Visual Cortex , 2006, Current Biology.

[36]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[37]  Yang Liu,et al.  A novel task-oriented optimal design for P300-based brain–computer interfaces , 2014, Journal of neural engineering.

[38]  Tobias Kaufmann,et al.  Beyond maximum speed—a novel two-stimulus paradigm for brain–computer interfaces based on event-related potentials (P300-BCI) , 2014, Journal of neural engineering.

[39]  Charles William Eliot,et al.  The Harvard classics , 2008 .