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Electroencephalography (EEG)-based neurofeedback training for brain–computer interface (BCI)

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An Erratum to this article was published on 12 February 2014

Abstract

Electroencephalography has become a popular tool in basic brain research, but in recent years, several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery, we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues, we here introduce a nontrivial modification to brain–computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real-time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop coadaptation system, we saw a progression of the cortical activation that started in sensorymotor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a directional signal in decision making tasks performed with intent.

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Acknowledgments

I would like to thank Dr. Elizabeth B. Torres for editing the manuscript.

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Authors and Affiliations

  1. Psychology Department, Computational Biomedicine Imaging and Modeling, Computer Science, Rutgers University, Busch Campus, 152 Frelinghuysen Rd., Piscataway, NJ, 08854, USA

    Kyuwan Choi

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  1. Kyuwan Choi
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Correspondence to Kyuwan Choi.

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Choi, K. Electroencephalography (EEG)-based neurofeedback training for brain–computer interface (BCI). Exp Brain Res 231, 351–365 (2013). https://doi.org/10.1007/s00221-013-3699-6

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  • DOI: https://doi.org/10.1007/s00221-013-3699-6

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