功能近红外光谱
计算机科学
人工智能
预处理器
神经影像学
机器学习
深度学习
管道(软件)
神经科学
认知
心理学
程序设计语言
前额叶皮质
作者
Condell Eastmond,Aseem Subedi,Suvranu De,Xavier Intes
出处
期刊:Neurophotonics
[SPIE - International Society for Optical Engineering]
日期:2022-07-20
卷期号:9 (04)
被引量:15
标识
DOI:10.1117/1.nph.9.4.041411
摘要
Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional near-infrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields.Aim: We aim to review the emerging DL applications in fNIRS studies.Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including brain–computer interface, neuro-impairment diagnosis, and neuroscience discovery.Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation.Conclusions: The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.
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