Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance

Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.

[1]  M. Srinivasan,et al.  Tactile detection of slip: surface microgeometry and peripheral neural codes. , 1990, Journal of neurophysiology.

[2]  Connor Esterwood,et al.  A Usability Study of Low-Cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model , 2020, IEEE Transactions on Human-Machine Systems.

[3]  Victor V. Kryssanov,et al.  Vibrotactile stimulus frequency optimization for the haptic BCI prototype , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[4]  Tobias Kaufmann,et al.  Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state , 2013, Front. Neurosci..

[5]  Álvaro Fernández-Rodríguez,et al.  Impact of Speller Size on a Visual P300 Brain-Computer Interface (BCI) System under Two Conditions of Constraint for Eye Movement , 2019, Comput. Intell. Neurosci..

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

[7]  Shoji Makino,et al.  Multi-command Chest Tactile Brain Computer Interface for Small Vehicle Robot Navigation , 2013, Brain and Health Informatics.

[8]  Peter Desain,et al.  Introducing the tactile speller: an ERP-based brain–computer interface for communication , 2012, Journal of neural engineering.

[9]  Victor Hugo C. de Albuquerque,et al.  A proposal for Internet of Smart Home Things based on BCI system to aid patients with amyotrophic lateral sclerosis , 2018, Neural Computing and Applications.

[10]  M. Hollins,et al.  Pacinian representations of fine surface texture , 2005, Perception & psychophysics.

[11]  Febo Cincotti,et al.  Tactile, Visual, and Bimodal P300s: Could Bimodal P300s Boost BCI Performance? , 2010 .

[12]  S. Ge,et al.  Experimental research on the tactile perception from fingertip skin friction , 2017 .

[13]  Xingyu Wang,et al.  An ERP-based BCI with peripheral stimuli: validation with ALS patients , 2020, Cognitive Neurodynamics.

[14]  Anne-Marie Brouwer,et al.  A tactile P 300 brain-computer interface , 2010 .

[15]  Mohamed Ghorbel,et al.  SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features , 2020, Comput. Intell. Neurosci..

[16]  T. Allison,et al.  Human extrastriate visual cortex and the perception of faces, words, numbers, and colors. , 1994, Cerebral cortex.

[17]  Andrzej Cichocki,et al.  Correlation-based channel selection and regularized feature optimization for MI-based BCI , 2019, Neural Networks.

[18]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[19]  Si Chen,et al.  Tactile perception of fabrics with an artificial finger compared to human sensing , 2015 .

[20]  A. Ehlis,et al.  N1 and N2 ERPs reflect the regulation of automatic approach tendencies to positive stimuli , 2013, Neuroscience Research.

[21]  Ian Daly,et al.  Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[23]  M. Hollins,et al.  Evidence for the duplex theory of tactile texture perception , 2000, Perception & psychophysics.

[24]  Andrzej Cichocki,et al.  Novel hybrid brain–computer interface system based on motor imagery and P300 , 2019, Cognitive Neurodynamics.

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

[26]  Xingyu Wang,et al.  Optimizing the Face Paradigm of BCI System by Modified Mismatch Negative Paradigm , 2016, Front. Neurosci..

[27]  Clemens Brunner,et al.  An adaptive P 300-based control system , 2011 .

[28]  A. Cichocki,et al.  Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns , 2020, Frontiers in Neuroscience.

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

[30]  S. Ge,et al.  Tactile perception of skin: research on late positive component of event-related potentials evoked by friction , 2020, The Journal of The Textile Institute.

[31]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[32]  A. Pavlovic,et al.  Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. , 2016, Journal of neurophysiology.

[33]  Steven Laureys,et al.  A tactile Brain-Computer Interface for severely disabled patients , 2014, 2014 IEEE Haptics Symposium (HAPTICS).

[34]  Shoji Makino,et al.  Spatial Tactile Brain-Computer Interface Paradigm Applying Vibration Stimuli to Large Areas of User's Back , 2014, ArXiv.

[35]  Ian Daly,et al.  Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[36]  Jing Jin,et al.  Effects of a Vibro-Tactile P300 Based Brain-Computer Interface on the Coma Recovery Scale-Revised in Patients With Disorders of Consciousness , 2020, Frontiers in Neuroscience.

[37]  Y. Iwamura Hierarchical somatosensory processing , 1998, Current Opinion in Neurobiology.

[38]  Xingyu Wang,et al.  Towards correlation-based time window selection method for motor imagery BCIs , 2018, Neural Networks.

[39]  Muhammad Nazrul Islam,et al.  Applying Brain-Computer Interface Technology for Evaluation of User Experience in Playing Games , 2019, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).

[40]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[41]  Qiang Gao,et al.  Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control , 2019, Comput. Intell. Neurosci..

[42]  J. Polich Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[43]  M Salvaris,et al.  Visual modifications on the P300 speller BCI paradigm , 2009, Journal of neural engineering.

[44]  Bernhard Schölkopf,et al.  An Auditory Paradigm for Brain-Computer Interfaces , 2004, NIPS.

[45]  Ren Xu,et al.  Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm , 2020, IEEE Transactions on Biomedical Engineering.

[46]  Jie Li,et al.  Design of assistive Wheelchair System directly Steered by Human Thoughts , 2013, Int. J. Neural Syst..