Tag: signal quality

Brain and Body: SPA-fNIRS Solves Physiological Signal Noise

Functional near-infrared spectroscopy (fNIRS) offers researchers a powerful noninvasive tool for studying brain activity by measuring changes in blood oxygen levels. These changes reflect how various brain regions respond to stimuli such as tasks that involve thinking, emotion, movement, and social interaction. These are often in natural, real-world settings that are not possible with other brain imaging methods. However, fNIRS is not without its own limitations. Systemic fluctuations in the body, such as breathing patterns, cardiovascular changes, and autonomic responses, can result in signal noise that is misinterpreted as brain activity by traditional fNIRS. The solution: a visit to the SPA.

Systemic Physiology Augmented fNIRS (SPA-fNIRS) is an advanced approach to fNIRS that goes beyond measuring local changes in cerebral oxygenation. While standard fNIRS measures cortical hemodynamics by tracking oxygenated (HbO) and deoxygenated (HbR) hemoglobin, noise originating from systemic physiological changes can corrupt those signals. Combining data from both neural and systemic physiological signals, SPA-fNIRS provides researchers with a more diverse and accurate view of how the brain and body interact in real time.

The method is being applied across various fields, including psychophysiology, cognitive neuroscience, exercise physiology, mental health, and human performance research. Studies have utilized SPA-fNIRS to investigate a range of topics, including meditation and emotional regulation, movement control, and social interaction. The broader goal is to move beyond studying the brain in isolation and to understand it as part of an integrated mind-body system. This holistic view is transforming how scientists interpret brain-function data, opening new paths for applied research in education, rehabilitation, and human-machine interface design.

soundResearchers in India investigated the impact of a traditional sound-based meditation technique, known as the Mind-Sound Resonance Technique (MSRT), on both brain and heart activity. Using fNIRS to track changes in oxygen levels in the prefrontal cortex, and a BIOPAC data acquisition and analysis system with AcqKnowledge software to record and analyze heart rate variability (HRV), the study employed a SPA-fNIRS design, integrating neural and physiological data in a single, synchronized dataset. HRV analysis allowed the researchers to quantify shifts in autonomic balance during and after meditation. The results showed increased oxygenation in the right prefrontal cortex and enhanced low-frequency HRV components, reflecting greater parasympathetic regulation. Together, these findings suggest that sound-based meditation not only improves brain oxygenation but also promotes calmer, more balanced heart-brain dynamics, illustrating how SPA-fNIRS methods can reveal the comprehensive physiological impact of mindfulness practices.

In her doctoral research at the Université de Lille in France, neuroscientist Aurore Guérin examined how the brain controls movement speed using a multimodal combination of fNIRS and physiological data recording systems. Participants performed repetitive arm movements at fast, natural, and slow tempos. At the same time, the study captured oxygenation in the prefrontal and motor cortices using fNIRS, alongside heart rate and respiration signals collected with a BIOPAC data acquisition system. A respiratory effort transducer measured breathing patterns, while a BioNomadix ECG module recorded cardiac activity for HRV analysis. All physiological signals were synchronized and analyzed in AcqKnowledge software, allowing the team to identify and remove systemic influences (e.g., heartbeat and breathing rhythms) from the fNIRS signals, following a SPA-fNIRS approach. The results revealed that faster movements primarily engaged motor regions, while slower, more deliberate pacing resulted in higher prefrontal activation, reflecting greater cognitive control. By integrating physiological monitoring with fNIRS, the study provided a clearer picture of how the brain and body coordinate timing and control during movement.

These studies illustrate how SPA-fNIRS is reshaping the way scientists investigate the interaction between brain and body. By combining neural and physiological data in a single synchronized system, researchers can separate brain activity from systemic noise and uncover the interplay between cognition, emotion, and autonomic regulation. For more information on this topic, watch our webinar on Multimodal SPA-fNIRS and check our our latest multimoldal fNIRS research products.

Are you planning a study that blends fNIRS with other physiological signals? Contact your local BIOPAC customer service representative to learn more about how SPA-fNIRS can benefit your research.

Noise Proof: Attack Artifacts Before They Ruin Data

After investing months of time, funds, and personal effort into your electrodermal activity (EDA) study, you begin combing through your data recordings when something catches your eye. Several of the recorded signals don’t look quite right. Rather than a narrow baseline in the signal graph, the baseline is thick and fuzzy, a sure sign of signal noise. To make matters worse, the recordings are broken by random peaks, artifacts unrelated to the EDA signal you sought to record. Noise and artifacts can corrupt research data, often rendering it useless. While it may be possible in some cases to clean up and salvage recorded data, such results often mean going back to square one and starting over.

The best option is to avoid bad data from the beginning. Several best practices can save researchers untold effort and expense by avoiding the pitfalls that can result in noise and artifacts. Noise and artifacts can be defined as unwanted changes to a signal caused by sources other than the target signal during recording. Noise and artifacts can originate from a range of sources—environmental (interference from other electromagnetic sources) or biological (signal changes from muscle activity in a participant), to name a few. The key is not to wait until all your data is recorded to tackle signal problems. Careful planning and testing procedures when designing and experimenting can avoid later headaches.

Most artifacts are caused by some form of electrical impedance interfering with a clean connection between the participant and the electrodes linking the data-gathering equipment. Sources could include dead skin or hair at the connection sight, dirty or improperly prepared electrodes, or electrodes that have come loose due to movement.

Using an impedance detector can help identify problematic signals before they corrupt data. An example of this can be found in a 2020 study at Ghent University in Belgium that examined how expert and novice map readers managed their cognitive loads using electroencephalogram (EEG) and eye tracking data. Researchers used a BIOPAC EL-CHECK impedance detector to ensure a good skin-electrode interface before recording data with AcqKnowledge software. This allowed the research team to keep the whole EEG circuit (ground, active EEG, and reference electrodes) at less than the recommended 10 K ohms (Herman, 2015; Teplan, 2002) to prevent unwanted signal noise.

noise checkStrategies for avoiding artifacts can vary depending on the type of signal being recorded. Some signals, such as EEG, may require light abrading of the skin at the point of electrode attachment for optimal conductance, while other signals, like EDA, do not require skin abrasion. Proper site preparation is recommended for most signals, skin should be clean and free of obstructions, such as excess hair. When gel is to be applied, make sure it is the correct type for the electrodes being used. Finally, the preparation of study participants can be critical to successfully recording clean data. In general, participants should be in an environment that is free of distractions and stimuli originating outside the study (sudden noises, distracting conversations, etc.).

For detailed information on how to proactively prevent signal noise and artifacts, check our AcqKnowledge Bootcamp webinar in which we discuss how to set up experiments to ensure quality data gathering. You can also consult our forthcoming guide with detailed instructions for the collection of optimal data.  Don’t wait until it’s too late; eliminating signal noise should be the first step in setting up a successful study.

Do you need help eliminating artifacts and signal noise in your recorded research data? BIOPAC’s regional sales representatives are ready to assist with tools and strategies to ensure you get only the best data possible. Contact us today!

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