Evaluation of color modulation in visual P300-speller using new stimulus patterns

Objective The stimulus color of P300-BCI systems has been successfully modified. However, the effects of different color combinations have not been widely investigated. In this study, we designed new stimulus patterns to evaluate the influence of color modulation on the BCI performance and waveforms of the evoked related potential (ERP).Methods Comparison was performed for three new stimulus patterns consisting of red face and colored block-shape, namely, red face with a white rectangle (RFW), red face with a blue rectangle (RFB), and red face with a red rectangle (RFR). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. Repeated-measures ANOVA and Bonferroni correction were applied for statistical analysis. Results The RFW pattern obtained the highest average online accuracy with 96.94%, and those of RFR and RFB patterns were 93.61% and of 92.22% respectively. Significant differences in online accuracy and information transfer rate (ITR) were found between RFW and RFR patterns (p < 0.05). Conclusion Compared with RFR and RFB patterns, RFW yielded the best performance in P300-BCI. These new stimulus patterns with different color combinations have considerable importance to BCI applications and user-friendliness.

[1]  Jingjing Yang,et al.  A Comparison of Facial P300-Speller Paradigm Based on Famous Face and the Familiar Face , 2019, 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[2]  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.

[3]  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..

[4]  Narendra D. Londhe,et al.  Power Spectrum Analysis of EEG Signals for Estimating Visual Attention , 2012 .

[5]  Xingyu Wang,et al.  A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..

[6]  D. A. Kirjanov,et al.  Visual Stimuli for P300-Based Brain-Computer Interfaces: Color, Shape, and Mobility , 2018, Moscow University Biological Sciences Bulletin.

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

[8]  T. Allison,et al.  Electrophysiological Studies of Face Perception in Humans , 1996, Journal of Cognitive Neuroscience.

[9]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[10]  Xingyun Dai,et al.  Explore Awareness of Information Security: Insights from Cognitive Neuromechanism , 2015, Comput. Intell. Neurosci..

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

[12]  Der-Song Lee,et al.  Color Combinations of Visual Display Terminal (VDT) Icon on User Preferences and EEG Response , 2010, Perceptual and motor skills.

[13]  Sebastian Halder,et al.  An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary , 2016, Front. Neurosci..

[14]  E. L. Schwartz,et al.  Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception , 1977, Biological Cybernetics.

[15]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[16]  Pushpendra Singh,et al.  A novel approach for automated alcoholism detection using Fourier decomposition method , 2020, Journal of Neuroscience Methods.

[17]  A. Cichocki,et al.  An optimized ERP brain–computer interface based on facial expression changes , 2014, Journal of neural engineering.

[18]  Hao Sun,et al.  Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm , 2020, Cognitive Neurodynamics.

[19]  M. Corbetta,et al.  Selective and divided attention during visual discriminations of shape, color, and speed: functional anatomy by positron emission tomography , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[20]  Chang S. Nam,et al.  Effects of Luminosity Contrast and Stimulus Duration on User Performance and Preference in a P300-Based Brain–Computer Interface , 2014, Int. J. Hum. Comput. Interact..

[21]  J. Luauté,et al.  BCI in patients with disorders of consciousness: clinical perspectives. , 2013, Annals of physical and rehabilitation medicine.

[22]  Nader Pouratian,et al.  A comparison of stimulus types in online classification of the P300 speller using language models , 2017, PloS one.

[23]  Mihaly Benda,et al.  Brain–Computer Interface Spellers: A Review , 2018, Brain sciences.

[24]  M. Eimer.  Event-related brain potentials distinguish processing stages involved in face perception and recognition , 2000, Clinical Neurophysiology.

[25]  Mitul Kumar Ahirwal,et al.  Development of Emotional Decision-Making Model Using EEG Signals , 2020, FICTA.

[26]  Qi Li,et al.  Time-varying networks of ERPs in P300-speller paradigms based on spatially and semantically congruent audiovisual bimodality , 2020, Journal of neural engineering.

[27]  G Pfurtscheller,et al.  EEG-based communication: improved accuracy by response verification. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

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

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

[31]  N. Birbaumer.  slow Cortical Potentials: Plasticity, Operant Control, and Behavioral Effects , 1999 .

[32]  Xingyu Wang,et al.  Investigation of Visual Stimulus With Various Colors and the Layout for the Oddball Paradigm in Evoked Related Potential-Based Brain–Computer Interface , 2019, Front. Comput. Neurosci..

[33]  Andrzej Cichocki,et al.  The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  S. Scott,et al.  Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. , 2001, Journal of neurophysiology.

[35]  Xingyu Wang,et al.  An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps , 2016, Cognitive Neurodynamics.

[36]  Yijun Wang,et al.  Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis , 2018, IEEE Transactions on Biomedical Engineering.

[37]  Xingyu Wang,et al.  An adaptive P300-based control system , 2011, Journal of neural engineering.

[38]  D. Jeffreys,et al.  The vertex-positive scalp potential evoked by faces and by objects , 2004, Experimental Brain Research.

[39]  Álvaro Fernández-Rodríguez,et al.  Evaluation of flashing stimuli shape and colour heterogeneity using a P300 brain-computer interface speller , 2018, Neuroscience Letters.

[40]  N. Birbaumer,et al.  fMRI Brain-Computer Interfaces , 2008, IEEE Signal Processing Magazine.

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

[42]  Ning Gao,et al.  A Convolutional Neural Network based on Batch Normalization and Residual Block for P300 Signal Detection of P300-speller System , 2019, 2019 IEEE International Conference on Mechatronics and Automation (ICMA).

[43]  J.J. Vidal,et al.  Real-time detection of brain events in EEG , 1977, Proceedings of the IEEE.

[44]  M Stemmler,et al.  Lateral interactions in primary visual cortex: a model bridging physiology and psychophysics. , 1995, Science.

[45]  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.

[46]  Xingyu Wang,et al.  A new P300 stimulus presentation pattern for EEG-based spelling systems , 2010, Biomedizinische Technik. Biomedical engineering.

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

[48]  Ivo Käthner,et al.  Rapid P300 brain-computer interface communication with a head-mounted display , 2015, Front. Neurosci..

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

[50]  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.

[51]  Stiliyan Kalitzin,et al.  Is colour modulation an independent factor in human visual photosensitivity? , 2007, Brain : a journal of neurology.

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

[53]  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.

[54]  C. C. Duncan,et al.  Event-related potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400 , 2009, Clinical Neurophysiology.

[55]  Xingyu Wang,et al.  Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern , 2017, Comput. Intell. Neurosci..

[56]  Guangli Li,et al.  Review of semi-dry electrodes for EEG recording , 2020, Journal of neural engineering.

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

[58]  Dewen Hu,et al.  Usage of drip drops as stimuli in an auditory P300 BCI paradigm , 2018, Cognitive Neurodynamics.