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前额叶不对称脑机接口神经反馈数据集。

Prefrontal Asymmetry BCI Neurofeedback Datasets.

作者信息

Charles Fred, De Castro Martins Caio, Cavazza Marc

机构信息

Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom.

School of Computing and Mathematical Sciences, University of Greenwich, London, United Kingdom.

出版信息

Front Neurosci. 2020 Dec 18;14:601402. doi: 10.3389/fnins.2020.601402. eCollection 2020.

Abstract

Prefrontal cortex (PFC) asymmetry is an important marker in affective neuroscience and has attracted significant interest, having been associated with studies of motivation, eating behavior, empathy, risk propensity, and clinical depression. The data presented in this paper are the result of three different experiments using PFC asymmetry neurofeedback (NF) as a Brain-Computer Interface (BCI) paradigm, rather than a therapeutic mechanism aiming at long-term effects, using functional near-infrared spectroscopy (fNIRS) which is known to be particularly well-suited to the study of PFC asymmetry and is less sensitive to artifacts. From an experimental perspective the BCI context brings more emphasis on individual subjects' baselines, successful and sustained activation during epochs, and minimal training. The subject pool is also drawn from the general population, with less bias toward specific behavioral patterns, and no inclusion of any patient data. We accompany our datasets with a detailed description of data formats, experiment and protocol designs, as well as analysis of the individualized metrics for definitions of success scores based on baseline thresholds as well as reference tasks. The work presented in this paper is the result of several experiments in the domain of BCI where participants are interacting with continuous visual feedback following a real-time NF paradigm, arising from our long-standing research in the field of affective computing. We offer the community access to our fNIRS datasets from these experiments. We specifically provide data drawn from our empirical studies in the field of affective interactions with computer-generated narratives as well as interfacing with algorithms, such as heuristic search, which all provide a mechanism to improve the ability of the participants to engage in active BCI due to their realistic visual feedback. Beyond providing details of the methodologies used where participants received real-time NF of left-asymmetric increase in activation in their dorsolateral prefrontal cortex (DLPFC), we re-establish the need for carefully designing protocols to ensure the benefits of NF paradigm in BCI are enhanced by the ability of the real-time visual feedback to adapt to the individual responses of the participants. Individualized feedback is paramount to the success of NF in BCIs.

摘要

前额叶皮质(PFC)不对称是情感神经科学中的一个重要标志,已引起广泛关注,与动机、饮食行为、同理心、风险倾向及临床抑郁症的研究相关。本文所呈现的数据是三项不同实验的结果,这些实验采用PFC不对称神经反馈(NF)作为脑机接口(BCI)范式,而非旨在产生长期效果的治疗机制,使用的是功能近红外光谱(fNIRS),众所周知,该技术特别适合研究PFC不对称且对伪迹不太敏感。从实验角度来看,BCI环境更强调个体受试者的基线、时段内成功且持续的激活以及最少的训练。受试者群体也来自普通人群,对特定行为模式的偏向较小,且未纳入任何患者数据。我们在数据集附带了数据格式、实验和方案设计的详细描述,以及基于基线阈值和参考任务对成功分数定义的个性化指标分析。本文所展示的工作是BCI领域多项实验的成果,在这些实验中,参与者按照实时NF范式与连续视觉反馈进行交互,这源于我们在情感计算领域的长期研究。我们向社区提供这些实验的fNIRS数据集。我们特别提供了从情感与计算机生成叙事交互领域的实证研究以及与启发式搜索等算法交互中获取的数据,这些都提供了一种机制,由于其逼真的视觉反馈,可提高参与者参与主动BCI的能力。除了详细介绍参与者接受背外侧前额叶皮质(DLPFC)激活左不对称增加的实时NF时所使用的方法外,我们重申需要精心设计方案,以确保实时视觉反馈适应参与者个体反应的能力能增强BCI中NF范式的益处。个性化反馈对于BCI中NF的成功至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86e1/7775574/cb38fe5a6c49/fnins-14-601402-g0001.jpg

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Prefrontal Asymmetry BCI Neurofeedback Datasets.前额叶不对称脑机接口神经反馈数据集。
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BCI Control of Heuristic Search Algorithms.脑机接口对启发式搜索算法的控制
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