Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System Using Wavelet Features and Various Machine Learning Methods

Steady-state visual evoked potentials (SSVEPs) have been designated to be appropriate and are in use in many areas such as clinical neuroscience, cognitive science, and engineering. SSVEPs have become popular recently, due to their advantages including high bit rate, simple system structure and short training time. To design SSVEP-based BCI system, signal processing methods appropriate to the signal structure should be applied. One of the most appropriate signal processing methods of these non-stationary signals is the Wavelet Transform. In this study, we investigated both the effect of choosing a mother wavelet function and the most successful combination of classifier algorithm, wavelet features, and frequency pairs assigned to BCI commands. SSVEP signals that were recorded at seven different stimulus frequencies (6–6.5 – 7 – 7.5 – 8.2 – 9.3 – 10 Hz) were used in this study. A total of 115 features were extracted from time, frequency, and time-frequency domains. These features were classified by a total of seven different classification processes. Classification evaluation was presented with the 5-fold cross-validation method and accuracy values. According to the results, (I) the most successful wavelet function was Haar wavelet, (II) the most successful classifier was Ensemble Learning, (III) using the feature vector consisting of energy, entropy, and variance features yielded higher accuracy than using one of these features alone, and (IV) the highest performances were obtained in the frequency pairs with “6–10”, “6.5–10”, “7–10”, and “7.5–10” Hz.

[1]  Y. Isler,et al.  Determining Gaze Information from Steady-State Visually-Evoked Potentials , 2020, Karaelmas Science and Engineering Journal.

[2]  Bijaya K. Panigrahi,et al.  A comparative study of wavelet families for EEG signal classification , 2011, Neurocomputing.

[3]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[4]  Yijun Wang,et al.  Visual and Auditory Brain–Computer Interfaces , 2014, IEEE Transactions on Biomedical Engineering.

[5]  icaii,et al.  Prediction of Evoking Frequency from Steady-State Visual Evoked Frequency , 2019 .

[6]  Athanasios V. Vasilakos,et al.  Brain computer interface: control signals review , 2017, Neurocomputing.

[7]  Jonathan R. Wolpaw,et al.  Brain Signals for Brain–Computer Interfaces , 2009 .

[8]  Brendan Z. Allison,et al.  Brain-Computer Interfaces: A Gentle Introduction , 2009 .

[9]  Chin-Teng Lin,et al.  Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients , 2018, IEEE Transactions on Fuzzy Systems.

[10]  Zhidong Deng,et al.  A CWT-based SSVEP classification method for brain-computer interface system , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[11]  Quan Liu,et al.  A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface , 2017, Journal of neural engineering.

[12]  Yue Zhang,et al.  Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review , 2021, IEEE Sensors Journal.

[13]  G.E. Birch,et al.  A general framework for brain-computer interface design , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  D. Regan,et al.  An Effect of Stimulus Colour on Average Steady-state Potentials evoked in Man , 1966, Nature.

[15]  Li Hongwei,et al.  Research on Steady State Visual Evoked Potentials based on Wavelet Packet Technology for Brain-Computer Interface , 2011 .

[16]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.

[17]  Serhat Ozekes,et al.  Harmonic analysis of steady-state visual evoked potentials in brain computer interfaces , 2020, Biomed. Signal Process. Control..

[18]  Zheng Wang,et al.  A Novel Instantaneous Phase Detection Approach and Its Application in SSVEP-Based Brain-Computer Interfaces , 2018, Sensors.

[19]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[20]  Reza Abiri,et al.  A comprehensive review of EEG-based brain–computer interface paradigms , 2019, Journal of neural engineering.

[21]  Yoonsuh Jung,et al.  A K-fold averaging cross-validation procedure , 2015, Journal of nonparametric statistics.

[22]  E. Basar EEG-brain dynamics: Relation between EEG and Brain evoked potentials , 1980 .

[23]  Shivam Srivastava,et al.  A new 360° rotating type stimuli for improved SSVEP based brain computer interface , 2020, Biomed. Signal Process. Control..

[24]  Yijun Wang,et al.  Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.

[25]  Li Zhang,et al.  A method for recognizing high-frequency steady-state visual evoked potential based on empirical modal decomposition and canonical correlation analysis , 2019, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[26]  Yasen Jiao,et al.  Performance measures in evaluating machine learning based bioinformatics predictors for classifications , 2016, Quantitative Biology.

[27]  Yalçın İşler,et al.  Uyartım frekansının kestiriminde istatistiksel anlamlılığa dayalı olarak seçilen durağan durum görsel uyarılmış potansiyellere ait dalgacık özniteliklerinin değerlendirilmesi , 2020 .