A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction

Junjie Bu; Junjie Bu; Chang Liu; Huixing Gou; Hefan Gan; Yan Cheng; Mengyuan Liu; Rui Ni; Zhen Liang; Guanbao Cui; Ginger Qinghong Zeng; Xiaochu Zhang; Xiaochu Zhang; Xiaochu Zhang; Xiaochu Zhang; Xiaochu Zhang

doi:10.3389/fnins.2021.647844

Frontiers in Neuroscience (Jul 2021)

A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction

Junjie Bu,
Junjie Bu,
Chang Liu,
Huixing Gou,
Hefan Gan,
Yan Cheng,
Mengyuan Liu,
Rui Ni,
Zhen Liang,
Guanbao Cui,
Ginger Qinghong Zeng,
Xiaochu Zhang,
Xiaochu Zhang,
Xiaochu Zhang,
Xiaochu Zhang,
Xiaochu Zhang

Affiliations

Junjie Bu: Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
Junjie Bu: Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
Chang Liu: Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
Huixing Gou: Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
Hefan Gan: Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
Yan Cheng: Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
Mengyuan Liu: Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
Rui Ni: Department of Life Sciences, Imperial College London, London, United Kingdom
Zhen Liang: Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China
Guanbao Cui: Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
Ginger Qinghong Zeng: Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
Xiaochu Zhang: Department of Radiology, The First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
Xiaochu Zhang: Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China
Xiaochu Zhang: Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
Xiaochu Zhang: Hefei Medical Research Center on Alcohol Addiction, Anhui Mental Health Center, Hefei, China
Xiaochu Zhang: Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China

DOI: https://doi.org/10.3389/fnins.2021.647844
Journal volume & issue: Vol. 15

Abstract

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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.

Published in Frontiers in Neuroscience

ISSN: 1662-4548 (Print); 1662-453X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: http://www.frontiersin.org/neuroscience

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