计算机科学
脑-机接口
运动表象
人工智能
任务(项目管理)
人机交互
作者
Foroogh Shamsi,Laleh Najafizadeh
出处
期刊:Springer International Publishing eBooks
[Springer Nature]
日期:2021-01-01
卷期号:: 1-32
标识
DOI:10.1007/978-3-030-67494-6_1
摘要
Brain-computer interfaces (BCIs) are developed to decode user’s intention from their brain activities. functional near-infrared spectroscopy (fNIRS) is a noninvasive, low-cost, and easy-to-use neuroimaging tool to measure brain activities in BCIs. An fNIRS-based BCI algorithm receives fNIRS recordings and employs classification techniques to decode the intended tasks. Designing a BCI classification algorithm which can accurately decode user’s intention involves selection of discriminative features from fNIRS recordings and a proper classifier model. Generally speaking, a BCI algorithm which can decode multiple tasks with high accuracy from short intervals of recordings is of great interest in BCI applications.
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