“Jumpingly” Perceive Time Series: Image Generation Approach to Modeling Functional Brain Activation

This paper presents a novel Linear Mapping Field (LMF) to map time series into two-dimensional images. The LMF extracts deeper features of fNIRS signals, which makes fNIRS less reliant on some prior. The developed convolution neural networks detect more prominent features than the state-of-the-art methods. The experimental results indicate that different from RNNs which can only perceive the time series in a “sequential” manner, LMF’s characteristic of “jumpingly” perception is the key to achieving excellent results.
Note to Practitioners
—As an optical and non-invasive technique to obtain the changes of oxyhemoglobin (O2Hb) and deoxyhemoglobin (HHb), fNIRS can be used to measure the changes of cerebral hemodynamics related to brain activities. This work proposes a linear mapping field and other mapping fields to map fNIRS signals to two-dimensional images, and the deep features of these generated images can be further extracted by convolutional neural networks, establishing an end-to-end bridge to detect the activation of brain functions under different tasks. Compared with mainstream methods, this work can be used as an effective mapping for fNIRS with low computational complexity and good performance.