Abstract
Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain–computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants’ smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.
Highlights
The brain–computer interface (BCI) is a hardware and software system integrated as the interface between the brain and the computer (Janapati et al, 2020)
We provide a novel dataset based on a cognitionguided, closed-loop, and individualized neurofeedback, which is based on multivariate pattern analysis (MVPA) classifier
The cue reactivity data were divided into two files: “cue reactivity_1.zip” and
Summary
The brain–computer interface (BCI) is a hardware and software system integrated as the interface between the brain and the computer (Janapati et al, 2020). Considering time sensitivity and device portability, BCI system generally uses electroencephalogram (EEG), electrocorticogram (ECoG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and positron emission tomography (PET) as imaging methods. EEG is the most widely used BCIs (Kwon et al, 2020). Cognition-Guided Neurofeedback BCI Dataset than EEG, but it is invasive (Korostenskaja et al, 2014). FMRI and fNIRS have high spatial resolution, their temporal resolution is low (Cui et al, 2011). MEG and PET require large and expensive equipment and are not suitable for large-scale applications (Stam, 2010)
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