Prediction of Event Related Potential Speller Performance Using Resting-State EEG

Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject performance instability and ERP-illiteracy are still unresolved. Therefore, it is necessary to predict classification performance before performing an ERP speller in order to use it efficiently. In this study, we investigated the correlations with ERP speller performance using a resting-state before an ERP speller. In specific, we used spectral power and functional connectivity according to four brain regions and five frequency bands. As a result, the delta power in the frontal region and functional connectivity in the delta, alpha, gamma bands are significantly correlated with the ERP speller performance. Also, we predicted the ERP speller performance using EEG features in the resting-state. These findings may contribute to investigating the ERP-illiteracy and considering the appropriate alternatives for each user.

[1]  Sung Chan Jun,et al.  P300 Speller Performance Predictor Based on RSVP Multi-feature , 2019, Front. Hum. Neurosci..

[2]  Seong-Whan Lee,et al.  Network Properties in Transitions of Consciousness during Propofol-induced Sedation , 2017, Scientific Reports.

[3]  Hyoung Joong Kim,et al.  A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes , 2016, IEEE Transactions on Information Forensics and Security.

[4]  Dinggang Shen,et al.  Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis , 2015, Brain Imaging and Behavior.

[5]  Klaus-Robert Müller,et al.  An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity , 2014, PloS one.

[6]  G. Knyazev EEG Correlates of Self-Referential Processing , 2013, Front. Hum. Neurosci..

[7]  S J Schiff,et al.  Performance predictors of brain–computer interfaces in patients with amyotrophic lateral sclerosis , 2016, Journal of neural engineering.

[8]  John Williamson,et al.  EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy , 2019, GigaScience.

[9]  K. Müller,et al.  Effect of higher frequency on the classification of steady-state visual evoked potentials , 2016, Journal of neural engineering.

[10]  Peng Xu,et al.  Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting- to task-state: Evidence from a simultaneous event-related EEG-fMRI study , 2020, NeuroImage.

[11]  Yangsong Zhang,et al.  Prediction of SSVEP-based BCI performance by the resting-state EEG network , 2013, Journal of neural engineering.

[12]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[13]  Heung-Il Suk,et al.  Subject and class specific frequency bands selection for multiclass motor imagery classification , 2011, Int. J. Imaging Syst. Technol..

[14]  John Williamson,et al.  A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Seong-Whan Lee,et al.  Changes of Functional and Effective Connectivity in Smoking Replenishment on Deprived Heavy Smokers: A Resting-State fMRI Study , 2013, PloS one.

[16]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[17]  Roberta Carabalona,et al.  The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces , 2017, Front. Neurosci..

[18]  S-W Lee,et al.  Biologically Motivated Computer Vision , 2000, Lecture Notes in Computer Science.

[19]  Minji Lee,et al.  Effective Correlates of Motor Imagery Performance based on Default Mode Network in Resting-State , 2020, 2020 8th International Winter Conference on Brain-Computer Interface (BCI).

[20]  P. Rossini,et al.  Hippocampal atrophy and EEG markers in subjects with mild cognitive impairment , 2007, Clinical Neurophysiology.

[21]  Ji-Hoon Jeong,et al.  Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Chang-Hyun Park,et al.  Motor imagery learning across a sequence of trials in stroke patients. , 2015, Restorative neurology and neuroscience.

[23]  Jun Kong,et al.  Spelling With a Small Mobile Brain-Computer Interface in a Moving Wheelchair , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Seong-Whan Lee,et al.  Connectivity differences between consciousness and unconsciousness in non-rapid eye movement sleep: a TMS–EEG study , 2019, Scientific Reports.

[25]  Minji Lee,et al.  A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task , 2020, 2020 8th International Winter Conference on Brain-Computer Interface (BCI).

[26]  Dinggang Shen,et al.  Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification , 2017, Human brain mapping.

[27]  Siamac Fazli,et al.  Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI , 2015, Pattern Recognit..

[28]  Fei Wang,et al.  Relationships between the resting-state network and the P3: Evidence from a scalp EEG study , 2015, Scientific Reports.

[29]  M Schürmann,et al.  Delta responses and cognitive processing: single-trial evaluations of human visual P300. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.