A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction

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.

Improved HHT-microstate analysis of EEG in nicotine addicts

“…The dataset used in this paper is derived from a novel cognition-guided neurofeedback BCI dataset on nicotine addiction, which includes smoking subjects performing two cognitively guided tasks at a sampling frequency of 250 Hz ( Bu et al, 2021 ). The cognitively guided task of the dataset is to record EEG data by allowing subjects to focus on the smoking-related pictures (e.g., holding a cigarette in hand) and paired neutral pictures (e.g., holding a pencil in hand).…”

Correlation between abnormal brain network activity and electroencephalogram microstates on exposure to smoking-related cues

“…The raw EEG data were bandpass filtered between 0.1 and 80 Hz, epoched from −1 to 90 s relative to the beginning of the cue onset, and baseline corrected using the interval from −1 to 0 s; 1 to 89 s of each epoch was selected and a conventional recursive least squares algorithm was used to correct for blink artefacts. 27 Then, the EEG data were segmented into 1 s epochs, and epochs containing amplitude changes exceeding ±100 mV were rejected. For the EEG microstate analysis, data were downsampled at 250 Hz, bandpass filtered between 2 and 20 Hz and re-referenced to the average reference.…”

Biofeedback Interventions for Impulsivity-related Processes in Addictive Disorders

“…Finally, it was observed that those participants who were more successful in the task also showed a higher decrease in craving scores. In a more recent study by the same group, the authors used the novel cognitionguided NFB brain-computer interface paradigm based on a cue-reactivity model to resolve the shortcomings of traditional NFB [41]. It has been reported that cue-reactivity leads to impulsive behavior in drug-seeking behavior as well as relapse; however, cue-reactivity has multiple EEG features including both time (e.g., P300, slow positive wave) and frequency-domain (e.g., alpha oscillation).…”

“…It has been reported that cue-reactivity leads to impulsive behavior in drug-seeking behavior as well as relapse; however, cue-reactivity has multiple EEG features including both time (e.g., P300, slow positive wave) and frequency-domain (e.g., alpha oscillation). The authors claim that, compared with the single signal EEG, this novel NFB process, which involves both an offline classifier construction and real-time NFB training, can better enhance sensitivity [41]. The participants were people with nicotine dependence.…”