Neuro-Tools : Heart Rate & Respiration November 21, 2016Posted by eyetrackrob in Biometric, Captiv, eye tracking, Market Research, Marketing, neuromarketing, TEA, Technology, Uncategorized.
Although not as fast as I thought, step by step, I’ll be covering the most relevant biofeedback sensors in this blog series. So far I’ve only managed to write about GSR, one of the sensors of the hour! Galvanic Skin Response has been around for a long time and in the past years it has gained lots of attention from researchers, but as you might have read in my last post, although it deserves all the attention it gets, it’s not always that simple to use.
Other measurements mentioned before that could tell you more about emotions or cognitive workload are respiration, heart rate and from this also the possibility to calculate the variability of the heart rate (HRV).
Heart Rate (HR) reflects the frequency of a complete heartbeat within a specific time window. It is typically expressed as beats per minute (bpm). The HR is constantly, antagonistically influenced by the sympathetic nervous system (SNS) and parasympathetic nervous system (PsNS) and in general heart rate, similar to GSR, unfolds rather slowly. Although with peak effects observed after 4 seconds and return to baseline after about 20 seconds it is much slower than GSR. Heart Rate Variability (HRV) on the other hand expresses the quick variations of the frequency between heart beats. The time between beats is measured in milliseconds (ms) and is called an “R-R interval” or “inter-beat interval (IBI).”
Image 1: shows a typical heart rhythm as recorded by an electrocardiogram (ECG). You can see heart rate (4bpm) as well as the differences in the inter-beat intervals.
Both measurements (HR and HRV) are closely related to emotional arousal, with HRV allowing for assessment of more sensitive and quicker changes, which also can be related to stress and cognitive workload (this might be a good topic for a follow up post).
While today many fitness devices exist that measure heart rate in the context of fitness and well being, those solutions might not be the ideal for your research. One of the reasons for this is the processing and averaging of data going on in the sensor.
Image 2: shows the same recording as averaged data export (blue) and as it was displayed during the recording (orange). The data was recorded with a wrist worn device measuring the HR optically using light. In the averaged data the highest heart rate is at around 100 bpm. In the live stream the same time frame shows much more variability (still averaging at around 100 bpm) and it’s clearly visible that it is not the highest value of the recording.
As mentioned above, heart rate has a relatively low sensitivity and slow response. Many wearable fitness trackers don’t allow to export the data for further analysis or allow to access only averaged data, where quick spikes in the data have been eliminated as noise. The result of this prepossessing of data is that the effects of emotion might be lost altogether. On the other hand to compute HRV correctly, continuous and precise measurements must be guaranteed. Just 2-3 missed data points can mean inaccurate calculations of the times between beats and thus again missing relevant events.
Image 3: In the live visualization the highest heart rate reaches 145bpm. However the suspiciously round form reaching to the peak value indicates that data points are missing and data was interpolated. This becomes clear when looking at the averaged data. This data would not be suited for interpretation of HR or HRV.
Another reason why many heart rate trackers available for fitness purposes are not necessarily a suitable solution for researchers is that most of them are worn on the wrist and use light to measure blood flow and from there derive the heart rate. Compared to sensors that are placed close to the heart and measure electrical impulses (electrocardiogram/ECG), sensors on the wrist have to overcome challenges of compensating for movements, muscle-tensing, sweating and potentially light interference. ECG sensors are therefore the recommended tool for data collection for research purposes as they are more sensitive to certain signal characteristics.
Image 4: ECG Sensor as belt or as electrodes
Research has associated respiration rate and depth with emotional impact and emotional valence. Interestingly olfactory information ascends directly to limbic areas and is not relayed through the thalamus as other sensory input. The Thalamus is a part of the brain which is acting as a relay and pre-processing for sensory information and is accounted to be relevant to regulate consciousness, arousal, wakefulness and alertness. As olfactory information is not relayed through this part of the brain, there is a different mechanism to make olfactory information conscious which leads to quicker physiological response and unconscious alternation of the respiratory pattern. Respiration patterns therefore allow to identify potentially unconscious liking or disliking and arousal. The deduction of a unique emotion from respiration rate and depth does not seem to be possible although more research is still needed in this area.
Respiration measurements can be obtained either from the use of dedicated clinical instruments, stretch sensitive respiration belts or can be calculated from ECG data. The latter being the least invasive for commercial research.
Figure 6. Stretch Sensitive Respiration Belt
ECG data can be processed in TEA Captiv to obtain HR, HRV and even respiration rate and as with GSR all of the mentioned measurements can be synchronized with eyetracking to understand what visual information influenced a rise in HR, a change in HRV or an alteration of respiration patterns.
In my next post I’ll take a look at how all these measurements can be combined and if through a combination it is possible to not only detect emotional events but also understand whether it is a positive or negative emotion and even which specific emotion it is. So, watch this space for more!