Investigating the Effect of Stimulus Type on Electroencephalogram Signal in a Brain-Computer Interface System with Interaction Error
Abstract
Purpose: Brain-Computer Interface (BCI) systems are a new channel of communication between human thoughts and machines without the aid of neuromuscular systems. Although BCI systems exhibit several advantages, they yet have a long road ahead to reach a flawless state. For example, error occurrence is one of the problems in these systems, leading to Error-Related Potentials (ErRP) in the brain signal. In this study, Electroencephalogram (EEG) signals in the condition of system error occurrence are investigated. Since local information on the EEG signal channels alone cannot reveal the secrets of the brain, functional connectivity was used as a feature in this research.
Materials and Methods: In this research, 32 channel EEG signals with a sampling frequency of 256 Hz were recorded from 18 participants while interacting with a BCI system which has an interaction error. Two types of stimulus were used, including visual and tactile ones. Moreover, the Inter-Stimulus-Interval (ISI) was changed during the task. After pre-processing, brain functional connectivity was calculated between all channel pairs in four groups (visual, tactile, combined visual-tactile (ISI=3.5), and combined visual-tactile (ISI=2)) using Magnitude-Squared Coherence (MSC) measure.
Results: The results showed a significant difference in frontal-right temporal connectivity between correct and error classes in tactile stimulation, in visual stimulation significant differences in frontal-occipital connectivity, in visual-tactile (ISI=3.5) stimulation significant differences in left temporal-occipital connectivity (P-value< 0.001), and in visual-tactile (ISI=2) stimulation significant differences in central-occipital connectivity were seen (P-value< 0.01).
Conclusion: This study shows that using brain functional connectivity features along with local features can improve the performance of BCI systems.
2- J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control.”, Clinical Neurophysiology, vol. 113, no. 6. Elsevier, pp. 767–791, (2002).
3- A. Buttfield, P. W. Ferrez, and J. D. R. Millán, “Towards a robust BCI: Error potentials and online learning.”, in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 164–168, (2006).
4- A. Nouri and K. Azizi, “Introducing a Convolutional Neural Network and Visualization of its Filters for Classification of EEG Signal for SSVEP Task,” Front. Biomed. Technol., vol. 7, no. 3, pp. 151–159, (2020).
5- H. Khajehpour, F. Mohagheghian, S. Bakht, N. Samadzadehaghdam, E. Eqlimi, and B. Makkiabadi, “Event-Related Potential Correlates of Biased Cognitive Processing and Control in Substance Abusers: A Review.”, Front. Biomed. Technol., vol. 6, no. 1, pp. 41–63, (2019).
6- G. Townsend, B. Graimann, and G. Pfurtscheller, “Continuous EEG classification during motor imagery - Simulation of an asynchronous BCI.”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 12, no. 2, pp. 258–265, (2004).
7- K. J. Friston, “Modalities, modes, and models in functional neuroimaging.”, Science, American Association for the Advancement of Science, vol. 326, no. 5951, pp. 399–403, (2009).
8- M. Grosse-wentrup, “Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing.”, NeurIPS Proceedings, pp. 561–568, (2009).
9- Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, et al. "Functional connectivity predicts MI-based BCI learning (oral presentation).", BCI 2021 - 8th International Meeting of the Brain-Computer Interface Society, (2021).
10- M. Billinger, C. Brunner, and G. R. Müller-Putz, “Single-trial connectivity estimation for classification of motor imagery data.”, J. Neural Eng., vol. 10, no. 4, p. 046006, (2013).
11- B. Ahkami, “Investigation of the effect of stimulus presentation interval on the error-related potential in electroencephalogram signal.”, Amirkabir(Polytechnique Tehran), (1397).
12- Ahkami B, Ghassemi F. "Adding Tactile Feedback and Changing ISI to Improve BCI Systems' Robustness: An Error-Related Potential Study.", Brain Topogr.; 34(4):467-477, (2021). doi: 10.1007/s10548-021-00840-6. Epub 2021 Apr 28. PMID: 33909193.
13- C. B. Holroyd and M. G. H. Coles, “The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity.”, Psychol. Rev., vol. 109, no. 4, pp. 679–709, (2002).
14- G. Bush, P. Luu, and M. I. Posner, “Cognitive and emotional influences in anterior cingulate cortex,” Trends in Cognitive Sciences, Elsevier Current Trends, vol. 4, no. 6, pp. 215–222, (2000).
15- O. Devinsky, M. J. Morrell, and B. A. Vogt, “Contributions of anterior cingulate cortex to behaviour.”, Brain, vol. 118, no. 1, pp. 279–306, (1995).
16- H. Zhang, R. Chavarriaga, M. K. Goel, L. Gheorghe, and J. D. R. Millan, “Improved recognition of error related potentials through the use of brain connectivity features.”, in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 6740–6743, (2012).
17- H. Gilbertson, L. Fang, J. A. Andrzejewski, and J. M. Carlson, “Dorsal anterior cingulate cortex intrinsic functional connectivity linked to electrocortical measures of error-monitoring.”, bioRxiv, p. 2020.10.21.348649, (2020).
18- A. Allahverdy, A. K. Moghaddam, M. R. Mohammadi, and A. M. Nasrabadi, “Detecting ADHD Children using the Attention Continuity as Nonlinear Feature of EEG.” Front. Biomed. Technol., vol. 3, no. 1–2, pp. 28–33, (2016).
19- F. Ghassemi, M. H. Moradi, M. T. Doust, and V. Abootalebi, “Combination of independent component analysis and feature extraction of ERP for level classification of sustained attention.”, 2009 4th Int. IEEE/EMBS Conf. Neural Eng. NER ’09, pp. 136–139, (2009).
20- S. Malekpour, J. A. Gubner, and W. A. Sethares, “Measures of generalized magnitude-squared coherence: Differences and similarities, J. Franklin Inst., vol. 355, no. 5, pp. 2932–2950, (2018).
21- M. Brázdil et al., “Directional functional coupling of cerebral rhythms between anterior cingulate and dorsolateral prefrontal areas during rare stimuli: A directed transfer function analysis of human depth EEG signal,” Hum. Brain Mapp., vol. 30, no. 1, pp. 138–146, (2009).
22- G. Spinelli, G. Tieri, E. F. Pavone, and S. M. Aglioti, “Wronger than wrong: Graded mapping of the errors of an avatar in the performance monitoring system of the onlooker.”, Neuroimage, vol. 167, pp. 1–10, (2018).
23- P. Luu, D. M. Tucker, D. Derryberry, M. Reed, and C. Poulsen, “Electrophysiological responses to errors and feedback in the process of action regulation.”, Psychol. Sci., vol. 14, no. 1, pp. 47–53, (2003).
24- Y. L. Yang, H. X. Deng, G. Y. Xing, X. L. Xia, and H. F. Li, “Brain functional network connectivity based on a visual task: Visual information processing-related brain regions are significantly activated in the task state.”, Neural Regen. Res., vol. 10, no. 2, pp. 298–307, (2015)
25- N. R. Wilson, D. Sarma, J. D. Wander, K. E. Weaver, J. G. Ojemann, and R. P. N. Rao, “Cortical Topography of Error-Related High-Frequency Potentials During Erroneous Control in a Continuous Control Brain–Computer Interface.”, Front. Neurosci., vol. 0, no. MAY, p. 502, (2019).
| Files | ||
| Issue | Vol 9 No 2 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v9i2.8852 | |
| Keywords | ||
| Electroencephalogram Brain-Computer Interface Error-Related Potentials Brain Functional Connectivity Statistical Analysis | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
