Hi everyone!
I'm looking for some exciting analysis methods for correlation, coherence, causality, etc. that can be used for physiological signals (time series, particularly RR time series) obtained from two subjects.
The case study that I consider is: is there any connection between the RR time interval series between a player on the instrument and the listener?
One of the methods that I want to use is the time-frequency coherence analysis by Orini et al. Article Assessment of the dynamic interactions between heart rate an...
where I will take to input two RR time interval series (from the player and the listener).What else can I use for this type of data? Do you have any recommendations?
I'm also interested in creating a connection network between players and listeners using the RR time interval series obtained from participants during a music session. However, I don't know what measure for such a graph will be good. It will be nice to create a network like in the Ivanov et al. methodology https://www.nature.com/articles/ncomms1705, not within a body but between the participants. What do you think?
Thank you for every suggestion.
Hi. i think act of instrument player effect on RR intervals and listener not have any movement. i think for measure heartbeat correlation between player and listener movement of subjects must be considered specially Wind Instruments.
I also want to add that there may be a third 'instance' added to the analysis, so the parameters of the music performance, such as the loudness or the tempo changes. These factors can affect either the player's or listener's hearts beats. Still, the question is to find an appropriate method to measure the connections between these three data sources (RR from the player, RR from the listener and music performance).
You may try the HeartMath website at https://www.heartmath.org/
A recent overview article follows:
Article HeartMath as an Integrative, Personal, Social, and Global He...
i am musician and played some instrument like (piano, guitar and flute) and i think about your great idea. if you want to find any relationship between player and listener you must use Pearson correlation analysis but before that you need to convert music elements (like tempo, interval, loudness) to one element with a algorithm then analyze that element with RR intervals of both player and listener. i think if you use Italian classic range of tempo as a score which with rise of tempo score increase (e.g. allegro, andante or something else) and for intervals first utilize major and minor scales as a reference and any passage between scales give a score based on interval changes for easy finding that you can utilize chord changes intervals.
also for data reduction i think you must converts RR intervals to means of RR in a fixed time like 10 sec then calculate music score on that 10 sec and then analyzed correlation of them with two tailed Pearson correlation analysis.
Can you correlate each of them with audio trace? They should respond to events in notes, the events may be both sounds and silence - n.b. timing of phrase of Miles Davies.
Hi Teodor, great idea; may I suggest recording of respiratory movements in both subjects?
In my private opinion blood pressure fires respiration which closes the loop. Official name is „sympathetic drive”. But this input is phasic with period of pressure wave. This assumption solves so many open questions!! But yes, to record respiration is a good idea.
my thesis did look at correlation and coherence Thesis Cardiorespiratory Optimized Guided-Breathing for Post-Stress...
Thank you all of you for the answers! For the study with the players, we have only ECG signals collected. However, we also have a data set of ECG, respiratory and BP signals (continuously measured) from the Listeners who listen to classical music. I'm interested in measuring that "sympathetic drive" Teodor Buchner mentioned above. That data set also allows us to look at the cardio-respiratory coupling. Do you have any other suggestions on what would be good to look at regarding connections between these three data sources?
If it possible to player and listener close their eyes. You can detect very nice correlation in alpha waves of brain with EEG monitoring i try it ;).
Good luck.
Mahdi Khorsand Ghaffari thank you for that suggestion! It could be very interesting. I don't know if the EEG signal from the heads of the players who are moving during playing would be good enough for tracking the brain waves, but maybe some good mobile EEG monitors would allow us to collect a good quality signal.
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