signals Archives - BIOPAC Blog

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.

Strategies 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!

Mind and Body: Signals to Enhance Your Psychophysiology Research

The human body and mind share an intrinsic link, both influencing and reflecting the health and general well-being of the other. Within the field of psychophysiology, researchers gain a wealth of insight into mental processes and disorders by studying physiological signals recorded from both the brain and body. Signals such as electrodermal activity (EDA), skin temperature (SKT), electroencephalogram (EEG), heart rate variability (HRV), and functional near-infrared spectroscopy (fNIRS) provide just a few of the tools helping us to better understand these mind-body interactions.

A wide range of research technologies has made these signals increasingly accessible in the lab as well as in everyday environments, allowing studies to place participants in increasingly naturalistic settings while gathering critical physiological data. In this way, researchers can provide a more accurate picture of how study participants react to stress and arousal, experience emotion, or interact within group environments.

One recent study used EDA and heart rate (HR) to examine the role of emotion in student success within computer-based learning environments (CBLE). As discussed in previous posts, a range of signals can be used to measure the body’s physiological response to emotion, otherwise known as “affect.” By gaining a better understanding of learners’ emotional responses to CBLE stimuli, researchers in this study hope to apply their data to improvements in CBLE design. EDA data was gathered from participants via SS57LA EDA leads while HR signals were collected with SS2LB shielded lead sets, both of which were connected to a BSL MP36 data acquisition and analysis system. Participants were shown imagery designed to evoke a negative emotional reaction before being asked to read a complex academic paper. The study was able to establish a link between the participants’ emotions and their comprehension of the reading material.

In another study, researchers used EDA and skin conductance response (SCR) to measure participants’ reactions to conditioned stimuli (CS) and unconditioned stimuli (US). The goal was to determine if fear-inducing memories could be modified either pharmacologically or through behavior modification to overcome fear. An MP36R System with AcqKnowledge software allowed analysis of SCR signals gathered via SS3LA finger transducers across a range of experiments designed to trigger fear-inducing memories. While the overall results were inconclusive, the SCR signals provided valuable feedback regarding the participants’ response to stimuli.

While EDA is a common thread through many of these psychophysiology studies, other signals provide researchers with a more complete and nuanced data set from which to draw their conclusions. Researchers exploring the factors influencing musical preferences combined EDA data with ECG and facial electromyography (fEMG) signals to assess how listeners reacted to familiar and unfamiliar musical stimuli. Data from all three signals were fed to an MP36 data acquisition and analysis system. The study found a strong correlation between musical preferences and familiarity with material as reflected by the subjects’ emotional responses.

These examples only scratch the surface of how psychophysiological research is being aided by an array of signals, all made accessible with available technology. To learn more about how to implement these signals in your next study, see BIOPAC’s Psychophysiology Applications webpage.

Contact your BIOPAC sales representative to learn more about the wide range of data-gathering tools available to help you construct your next psychophysiology study.

Evoked Potentials: Low-amplitude Signals for High-value Data

Researchers studying the nervous system have a host of physiological signals at their disposal to provide critical data on neurological function. Various signals present advantages and disadvantages, depending on the demands of the research being conducted. Evoked potentials offer a particularly effective means of measuring the nervous system’s response to certain stimuli, notably visual and auditory cues.

Evoked potentials are employed in both research and clinical settings to measure electrical activity in the central nervous system. Stimulation of specific sensory neural pathways produces this electrical activity, allowing researchers to see the speed and thoroughness with which these signals reach the brain. Evoked potentials provide researchers with heightened precision when isolating and localizing a participant’s response to stimuli.

Because evoked potential amplitudes tend to be low, from less than one to just a few microvolts, these signals can be difficult to differentiate from other higher amplitude signals and background noise. This is resolved by using signal averaging to separate the low-amplitude signals from the noise. Multiple readings are taken with evoked potential signals time-locked to the stimulus. Averaging these measurements allows the evoked potential to be filtered from the random signal noise.

Along with providing useful data for research, evoked potential tests are commonly used in clinical settings to diagnose and explore treatments for a range of disorders such as multiple sclerosis (MS), epilepsy, glaucoma, neuropathies, Alzheimer’s, and others. In both research and clinical applications, evoked potential tests provide several advantages over using other approaches since they are noninvasive, painless, and provide results more quickly than other neurological scans, such as MRI. Types of evoked potentials can be categorized by the stimuli with which they are associated. Visual evoked potentials (VEP) are triggered by visual stimuli such as light flashes or by participants viewing certain patterns. Auditory evoked potentials (AEP) measure responses to sound stimuli such as clicks. Somatosensory evoked potentials (SEP) are triggered by electrical stimulation of peripheral nerves.

A 2022 study by researchers at the Autonomous University of Aguascalientes in Mexico evaluated the effectiveness of using VEP waveforms embedded in electroencephalographic (EEG) signals to determine the health of the optic nerve. The team employed an MP36 data acquisition system connected to an SS2LB lead set to record EEG signals from study participants. The signal data were used to create a database of VEPs that could be processed with analysis software for the early detection of glaucoma and other optic nerve-related illnesses.

A similar study combined the use of EEG signals with VEP to examine how participants recognize em otion in others. Researchers from two universities in China relied on a variation of VEP known as steady-state visual evoked potential (SSVEP), which exhibits distinctive frequency and phase properties. EEG signals were recorded via a BIOPAC data acquisition unit while participants were shown flickering images representing three emotional states—delight, sadness, and anger. The study found a positive correlation between SSVEP amplitude, alpha-frequency entrainment, and accurate identification of emotion.

These are just a few examples of how evoked potentials provide researchers with valuable insight into the central nervous system’s response to stimuli and its relationship to a wide range of physiological states. For additional information on how to use BIOPAC hardware and AcqKnowledge to stimulate a range of senses using electrical, auditory, visual, haptic, scent, and thermal stimuli and record evoked potentials, check out our webinar on Exogenous and Stimulus-related Evoked Potentials.

To learn more about how your study can benefit from using BIOPAC hardware and software to record and analyze evoked potential data, contact your local sales representative.

Choosing the Right Signal: Easy as 1, 2, 3

In physiology and life sciences, researchers rely on a wide range of signals to gather data from humans and animals. These signals provide critical insight into how complex organisms function under a myriad of conditions. However, this wealth of potential data sources can also present a challenge for researchers: choosing the right signals on which to focus their data gathering. For those lacking a background in the disciplines and mechanisms behind these signals, sifting through the available methodology can be overwhelming. The best place to begin is by considering one or a combination of the “Big Three” physiological signals: ECG, EDA, and respiration.

One from the Heart

Changes in heart rate can provide information on both the physiological and psychological state of a research participant. Electrocardiography (ECG or EKG) is a biopotential measurement that provides direct metrics on electrical voltage changes in the heart. With the help of simple hardware and software, it is possible to record these voltage variations as waveforms to track how the heart responds to stimuli. Additional data can be provided by creating reoccurring reference points in the waveform, measuring the intervals between beats, and calculating heart rate variability to track changes over time or in response to stimuli.

Skin Deep

Electrodermal Activity (EDA) provides one of the best measurements of how the body responds to psychological stress and anxiety. It can be simply understood as a measure of changes in the electrical conductance of the skin in relation to the production of sweat. The physiological process of EDA comprises two components, skin conductance level (SCL) and skin conductance response (SCR). SCL is a tonic level that slowly changes over time, measuring the period for response to a stimulus to occur, from its onset to its peak. SCR reflects the phasic response to an arousing stimulus that is shown in faster changes of the skin conductance level. As an example of the application of EDA and ECG measurements in psycho-physiological research, a recent study applied both signals in measuring the cognitive states of gamers during human-computer interaction (HCI).

Breathe Easy

As with changes in heart rate, variations in breathing cycle reveal how subjects respond to a wide range of stimuli and conditions. The signal produced by the respiratory cycle is a transduced physiological signal. As such we can apply mechanisms to create a voltage potential to measure phases of inhalation and exhalation in this cycle. This data can be represented in waveforms, providing data on respiratory rate, amplitude, and respiratory sinus arrhythmia (RSA). The latter combines respiratory data with ECG signal data to show changes in heart rate in response phases in the respiratory cycle. A 2020 machine learning study for the development of virtual reality applications combined respiration, EDA, and ECG signals to measure participant’s fear response to spiders, an example of how all three signals can be simultaneously utilized in psychophysiology research.

Choosing the right signal shouldn’t be the biggest hurdle to designing your next research project. Applying one or more of these “Big Three” can cover a wide range of data gathering goals.

For additional assistance, consult BIOPACS online resources on Life Science and Physiological Signals or contact your local BIOPAC representative.