BrainKilter: A Real-Time EEG Analysis Platform for Neurofeedback Design and Training

Guangying Pei, Guoxin Guo, Duanduan Chen, Ruoshui Yang, Zhongyan Shi, Shujie Wang, Jinpu Zhang, Jinglong Wu, Tianyi Yan

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Neurofeedback targets self-regularized brain activity to normalized brain function based on brain-computer interface (BCI) technology. Although BCI software or platforms have continued to mature in other fields, little effort has been expended on neurofeedback applications. Hence, we present BrainKilter, a real-time electroencephalogram (EEG) analysis platform based on a '4-tier layered model'. The purposes of BrainKilter are to improve portability and accessibility, allowing different users to choose various options to perform EEG processing, target stimulation-induction through a pipeline, and analyze data online, essentially, to design a protocol paradigm and applicable BCI technology for neurofeedback experiments. The data processing effectiveness and application value of BrainKilter were tested using multiple-parameter neurofeedback training, in which BrainKilter regulated the amplitude of mismatch negative (MMN) signals for healthy individuals. The proposed platform consists of a set of software modules for online protocol design and signal decoding that can be conveniently and efficiently integrated for neurofeedback design and training. The BrainKilter platform provides a truly easy-to-use environment for customizing the experimental paradigm and for optimizing the parameters of neurofeedback experiments for research and clinical neurofeedback applications using BCI technology.

Original languageEnglish
Article number8963696
Pages (from-to)57661-57673
Number of pages13
JournalIEEE Access
Publication statusPublished - 2020


  • BCI
  • BrainKilter
  • MMN
  • neurofeedback
  • platform
  • real-time

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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