Abstract
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.
Highlights
Bain-computer interfaces (BCI) based on electroencephalogram (EEG) signals seek to transform the user’s brain activities into computer commands [1]
The mismatch negative (MMN) amplitude of the training period was monitored in real time, and we provided the normalization of the MMN as a visual feedback signal to the user in real time
1100 Hz was used as the standard stimulus for all individuals, and the individual’s double auditory frequency threshold was used as the deviant stimulus
Summary
Bain-computer interfaces (BCI) based on electroencephalogram (EEG) signals seek to transform the user’s brain activities into computer commands [1]. The basic neurofeedback procedures have been established for quite a long time, they and most likely represent even the earliest BCI applications, which targeted the self-normalizing brain function [2]. BCIs in other fields have continued to mature and BCI software has expanded and gained strong support, methodological and technical progress with neurofeedback seems to be lagging [3]. From their inception, neurofeedback procedures piqued researchers’ interests, who focused on its application for clinical treatment and cognitive modulation. An advanced neurofeedback platform will promote neural mechanism for research and clinical applications
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