Application of wearable fNIRS for robust real-life measurements
Daphne van der Putte, Jörn Horschig and Willy Colier, Artinis
Duration: 180 min
Capacity: 30
Level: Introductory
Demo Tech. and Toolbox
Requirements: None
Synopsis: Currently, wearable fNIRS devices play an important part in many cognitive research fields, because it allows measuring brain activity during real-life situations without restrictions. It is currently being discovered in sports science, geriatric, pediatric, cognitive neuroscience and entertainment research, where it helps answer new types of research questions. Here, we will introduce wearable Artinis fNIRS devices using innovative, trending, and interactive tools. We will show and discuss our solutions for measuring all ages: from babies to the elderly. Get hands-on experience with the devices using a fun example experiment while enjoying the comfort and flexibility of wearable fNIRS. You will have the opportunity to move around during your data collection. Learn how to handle motion artefacts, integrate short separation channel data, analyse the data reliably using OxySoft, and process your data in real time with the help of Lab Streaming Layer (LSL).
Rationale: The advent of truly wearable fNIRS devices is important for progressing to more naturalistic paradigms in cognitive research fields (Pinti et al., 2018). For example, for a functional brain-computer interface (BCI) to be used in natural settings it must provide a robust and reliable output (van Gerven et al., 2009). Most of the non- invasive BCI research focuses on using electrophysiological measurements. For real-life measurements this is problematic because our daily environment is full of electromagnetic noise, and electrophysiological measurements are usually highly susceptible to movement artefacts. As our wearable fNIRS devices suffer less from such artefacts, they are perfectly suited for providing robust data not only in this scenario, but also many other real-life scenarios. We would like to demonstrate how these wearable fNIRS devices and the latest software can provide real-time input signals for a neurofeedback system. Additionally, to help future research to flourish and accomplish the highest feats possible, we would like to share our knowledge on how to work with the devices: how to obtain good quality data, how to analyse data, and how to set up your research paradigm in the most effective way possible.
Learning objectives: Participants will learn how to set up a robust and reliable experimental paradigm using wearable fNIRS devices, what features of the devices are suitable for specific research goals and environments, and how to cope with movement artifacts. They will learn how to use the latest software and how to use open-source tools like LSL to set up a streaming pipeline for real-time data analysis. They will get hands-on experience with the devices by setting up and performing a simple example experiment that combines most of the learning goals. At the end of the course, the participants will know how to use the wearable devices to their full potential.