In Artinis NIRS blog, you will find the latest trends in (f)NIRS, NIRS studies and applications, tutor from the leaders of near infrared spectroscopy, not to mention detailed insights and tips and tricks for your research!
Search blog post topic
Multimodal fNIRS-EEG measurements — Analysis approaches
When it comes to deciding on an appropriate data analysis approach in multimodal fNIRS-EEG measurements, the soundest consideration factors ultimately depend on the research question at hand. Therefore, the analysis steps may vary from one study to another. Nonetheless, they can broadly be classified into two strategies: parallel data analysis and informed data analysis.
Multimodal fNIRS-EEG measurements — Staying in sync
In this second blog post, we discuss synchronization in multimodality. When using fNIRS and EEG simultaneously, synchronization of both data streams is crucial to achieve temporal correspondence between both signals. Read this post to learn more about how to get to the ideal synchronization solution for you, which can depend on different factors, such as device specifications, software capabilities and experimental setup.
Introduction to multimodal fNIRS — EEG measurements
In this blog post, we give an introduction to multimodality and explain the hallmarks, advantages, and challenges of combining fNIRS and EEG to measure brain activity. This is the first part of a blog post series on multimodality fNIRS-EEG measurements.
The Starstim fNIRS - Combining tES brain stimulation and EEG + fNIRS neuroimaging in one headcap
Starstim fNIRS is the most adaptable solution to combine tES brain stimulation with EEG and fNIRS neuroimaging in one single wireless and wearable system – and what makes it the most versatile solution for researchers and clinicians in many application areas. The possibility to combine tES with multiple neuroimaging modalities in one device facilitates altering human behavior and acquiring a more complete picture of the brain. It further increases application possibilities and reduces set-up and measurement time.
Heart rate extraction from NIRS signal
A commonly asked question is “What are the advantages of NIRS over EEG?”. NIRS signals are, in general, less susceptible to artifacts like motion artifacts or electrical noises. There are, however, also physiological components such as heartbeat, breathing, and Mayer-waves present in the signal. Although not an artifact, these components are usually filtered out since they are not useful in determining the pure hemodynamic response signal of the brain. Nevertheless, there is interesting information in the heartbeat. In this blog, we talk about taking advantage of the heartbeat in the NIRS signals and extracting the heart rate signal from them.
Exercise Physiology integrating NIRS, ECG, and EMG as a learning tool
Innovations travel back and forth between different departments within Artinis. Application Specialists give insight in customer requests, Research and Development find new measurement methods, Design and Engineering constantly look for further improvement of the equipment and Sales and Support find new ways to support every single customer. Especially for new employees, hands-on experience throughout the company is crucial in their development and no better way to do this than to set up a study.
PROMPT project: towards personalized treatment of mobility dysfunction
In this project we will focus on one of the most disabling symptoms of Parkinson’s disease, freezing of gait – episodic absence or reduction in the ability to produce an effective stepping in spite of the intention to walk (Nutt et al., 2011).
Combining the world of NIRS and EEG
EEG and fNIRS are complementary measuring techniques. EEG measures electrophysiological brain activation, that is the electromagnetic field created when neurons in the brain are firing. fNIRS measures the hemodynamic response, that is the change of oxygen in the blood when a brain region becomes active. By combining EEG and fNIRS, a more complete picture of brain activity is obtained: activation of neurons and energy demand of neurons.