Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenarios

There are several protocols in the Electroencephalography (EEG) recording scenarios which produce various types of event-related potentials (ERP). P300 pattern is a well-known ERP which produced by auditory and visual oddball paradigm and BCI speller system. In this study, P300 and non-P300 separability are investigated in two scenarios including image recognition paradigm and BCI speller. Image recognition scenario is an experiment that examines the participants, knowledge about an image that shown to them before by analyzing the EEG signal recorded during the observing of that image as visual stimulation. To do this, three types of famous classifiers (SVM, Bayes LDA, and sparse logistic regression) were used to classify EEG recordings in six classes problem. Filtered and down-sampled (temporal samples) of EEG recording were considered as features in classification P300 pattern. Also, different sets of EEG recording including 4, 8 and 16 channels and different trial numbers were used to considering various situations in comparison. The accuracy was increased by increasing the number of trials and channels. The results prove that better accuracy is observed in the case of the image recognition scenario for the different sets of channels and by using the different number of trials. So it can be concluded that P300 pattern which produced in image recognition paradigm is more separable than BCI (matrix speller).

[1]  Misha Pavel,et al.  A framework for rapid visual image search using single-trial brain evoked responses , 2011, Neurocomputing.

[2]  Brian Roark,et al.  RSVP keyboard: An EEG based typing interface , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Sepideh Hajipour Sardouie,et al.  The Effect of Constant and Variable Stimulus Duration on P300 Detection , 2019, 2019 27th Iranian Conference on Electrical Engineering (ICEE).

[4]  Keum-Shik Hong,et al.  Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis , 2011, Biomedical engineering online.

[5]  Behzad Nazari,et al.  A single channel-single trial P300 detection algorithm , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[6]  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.

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

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

[9]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[10]  Benjamin Blankertz,et al.  Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP) , 2013, Clinical Neurophysiology.

[11]  Mohammad Bagher Shamsollahi,et al.  A novel dual and triple shifted RSVP paradigm for P300 speller , 2019, Journal of Neuroscience Methods.

[12]  Gunnar Blohm,et al.  A New Method for EEG-Based Concealed Information Test , 2013, IEEE Transactions on Information Forensics and Security.

[13]  Gernot R. Müller-Putz,et al.  Brain-controlled applications using dynamic P300 speller matrices , 2015, Artif. Intell. Medicine.

[14]  Febo Cincotti,et al.  Hybrid P300-based brain-computer interface to improve usability for people with severe motor disability: electromyographic signals for error correction during a spelling task. , 2015, Archives of physical medicine and rehabilitation.

[15]  Lucas C. Parra,et al.  High-throughput image search via single-trial event detection in a rapid serial visual presentation task , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

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

[17]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[18]  Benjamin Blankertz,et al.  A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System , 2011, Front. Neurosci..

[19]  J. A. Elshout.  Review of Brain-Computer Interfaces based on the P300 evoked potential , 2009 .

[20]  J. Polich,et al.  P300 amplitude is determined by target-to-target interval. , 2002, Psychophysiology.

[21]  Wenjia Zhang,et al.  A novel classification method based on ICA and ELM: a case study in lie detection. , 2014, Bio-medical materials and engineering.

[22]  Mohammad Bagher Shamsollahi,et al.  Comparison between Single, Dual and Triple Rapid Serial Visual Presentation Paradigms for P300 Speller , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[23]  Liang Lu,et al.  A Novel Concealed Information Test Method Based on Independent Component Analysis and Support Vector Machine , 2012, Clinical EEG and neuroscience.

[24]  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.