An Tactile ERP-Based Brain–Computer Interface for Communication

ABSTRACT A classical visual event relative potential (ERP) brain–computer interface (BCI) system relies on visual stimuli to choose commands. Users obtain most information about their surroundings visually as well. This large amount of information can aggravate visual burden and fatigue. In our study, we proposed a novel approach to evoke ERP with a tactile stimulus. To achieve this approach, we first designed a wireless stimulus module with vibrators to provide a tactile stimulus for the system. The vibrators were located on the subject’s arm to imitate the joint motion of a robotic arm. Then, the ERP feature and the parameters of classifiers were obtained through offline experimental data analysis. Based on the analysis, the suitable electrode channels, stimulus onset asynchrony (SOA), and filter upper limit were different for different subjects. According to those outcomes, a unique classifier was designed for each subject. Finally, 10 healthy BCI-naive subjects participated in online experiments to evaluate the performance of our tactile BCI system; they achieved an accuracy range from 78.67% to 100% with an average of 89.1% and an instantaneous transmission rate (ITR) range from 7.77 to 28.70 bits/min with an average of 14.77 bits/min. The accuracy of different subjects and SOAs remained relatively stable, the ITR fluctuated mainly due to the different SOAs, and we achieved balance between ITR and accuracy.

[1]  J. Wolpaw,et al.  P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls , 2015, Clinical Neurophysiology.

[2]  Bettina Forster,et al.  When far is near: ERP correlates of crossmodal spatial interactions between tactile and mirror-reflected visual stimuli , 2011, Neuroscience Letters.

[3]  Y. Nakajima,et al.  Visual stimuli for the P300 brain–computer interface: A comparison of white/gray and green/blue flicker matrices , 2009, Clinical Neurophysiology.

[4]  L. Skovgaard,et al.  Tactile and visual evaluation of the response to train-of-four nerve stimulation. , 1985, Anesthesiology.

[5]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[6]  A. Kübler,et al.  Training leads to increased auditory brain–computer interface performance of end-users with motor impairments , 2016, Clinical Neurophysiology.

[7]  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.

[8]  Jan B. F. van Erp,et al.  A Tactile P300 Brain-Computer Interface , 2010, Front. Neurosci..

[9]  Nazih Mechbal,et al.  An acoustic multi-touch sensing method using amplitude disturbed ultrasonic wave diffraction patterns , 2010 .

[10]  S. Silvoni,et al.  Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface , 2016, Clinical Neurophysiology.

[11]  David T. Kemp,et al.  Effect of contralateral auditory stimuli on active cochlear micro-mechanical properties in human subjects , 1990, Hearing Research.

[12]  Fanglin Chen,et al.  A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm , 2013, Journal of neural engineering.

[13]  Fanglin Chen,et al.  A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP , 2014, IEEE Transactions on Biomedical Engineering.

[14]  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.

[15]  Nader Pouratian,et al.  A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems , 2015, Clinical Neurophysiology.

[16]  Dennis J. McFarland,et al.  The P300-based brain–computer interface (BCI): Effects of stimulus rate , 2011, Clinical Neurophysiology.

[17]  Yuanqing Li,et al.  An Online Semi-supervised Brain–Computer Interface , 2013, IEEE Transactions on Biomedical Engineering.

[18]  B.Z. Allison,et al.  ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Tae-Seong Kim,et al.  An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier , 2015, Comput. Biol. Medicine.

[20]  G Pfurtscheller,et al.  EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components. , 1995, Medical progress through technology.

[21]  Jérémie Mattout,et al.  Improving BCI performance through co-adaptation: applications to the P300-speller. , 2015, Annals of physical and rehabilitation medicine.

[22]  Jon Driver,et al.  Rapid enhancement of touch from non-informative vision of the hand , 2012, Neuropsychologia.

[23]  H. van der Kooij,et al.  Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Tae-Seong Kim,et al.  A P300-based brain computer interface system for words typing , 2014, Comput. Biol. Medicine.

[25]  A. Frolov,et al.  Brain-Computer Interface Based on Generation of Visual Images , 2011, PloS one.

[26]  Ying Sun,et al.  Adaptation in P300 Brain–Computer Interfaces: A Two-Classifier Cotraining Approach , 2010, IEEE Transactions on Biomedical Engineering.

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

[28]  M. Castelo‐Branco,et al.  Comparison of a row-column speller vs. a novel lateral single-character speller: Assessment of BCI for severe motor disabled patients , 2012, Clinical Neurophysiology.

[29]  Andrés Úbeda,et al.  SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks , 2015, Neurocomputing.

[30]  Hubert Cecotti,et al.  Spelling with non-invasive Brain–Computer Interfaces – Current and future trends , 2011, Journal of Physiology-Paris.

[31]  Tomasz M. Rutkowski,et al.  Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users , 2014, Journal of Neuroscience Methods.