Author(s): Omar Ahmed Alsuhaibani, Pamela Benitez, Cynthia Love
Mentor(s): Nathalia Peixoto, Bioengineering
https://youtu.be/oK0Bic28M-k
This study explored the efficacy of computer-generated music in comparison to human-composed (commercial) music among students and examined how music tempo and style related to changes in physiological signals such as heart rate and EEG signals from the brain. Participants were 6 students ranging from ages 15 to 22 years old, both male and female. During the time of testing, participants were given ten six-minute tests: one as a baseline reading of physiological signals, three tests involving commercial or human-generated music of varying tempos, and 6 tests involving the computer-generated music at different speeds and styles. Results suggested that computer-composed music is (a) positively correlated with a change in physiological signals, (b) more effective than human-composed music, and lastly that music tempo had a (c) positive correlation with gamma and beta brain waves. Results are discussed in terms of implications for students’ changes in heart rate and EEG/brain wave activity.
Hello, we are one of the groups taking part in the STIP program for the summer of 2022.
Our group consists of three members that worked on this research project over the summer. My name is Cynthia Love, serving as the project manager. I am also working alongside Omar Alsuhaibani and Pamela Benitez. The purpose of our group’s project was to explore how different styles, sources, and speeds of music affect physiological signals such as EEG signals from the brain or heart rate.
Some real life applications are to gain the ability to measure emotions, something that has not yet been easily quantifiable. Another application is to optimize an up and coming field of therapy known as music therapy, especially for those with auditory triggers. While music therapy has grown in popularity for its ability to effectively reduce feelings of anxiety and stress, it was unknown if computer-generated music was as effective as music composed by humans. This was another research question we had in mind during our experiments.
Our group hypothesized that there would be a positive correlation between music tempo and beta and gamma brain waves. We hypothesized that fast music would enhance beta and gamma brain waves, while slow music would suppress them. Our last hypothesis is that computer-generated music would be less effective at influencing physiological signals.
Materials – The equipment used for our experiments is listed on the screen. The muse headband was used to take EEG brainwaves, while the empatica wristband was used to record heart rate. The mind monitor app allowed us to process the raw EEG data gathered from the experiments. The E4 connect software was used to process the heart rate data. The Interamusi Software is where we derived most of our music from. It is a website that composes custom music and allows it to be played on a loop.
Testing – this is the type of software that we used for each data processing. The one on the left is the empatica real-time app, which was used to process heart rate data. The app on the right is the mind monitor app, which was used to process EEG data.
Methods – For this experiment, we first started with a baseline test. In this portion, participants were instructed to sit in an empty room, with their eyes closed. Lights were turned off in the room to prevent any electrical interference with the equipment used. The Muse 2 headband was placed onto the participant, as well as the E4 on the participant’s wrist. Both devices were turned on and captured the participant’s readings for 6 minutes. The next portion involved the music tests. Participants listened to each of the 9 computer-generated songs for 6 minutes each. These songs were split into two categories, computer-generated classic music, and computer-generated modern music. There were then three subcategories, known as calm, stimulus, and focused. The order in which the music was played was randomized for every single participant.
For each stimulus, the Muse 2 headband and E4 were activated at the start of each 6-minute trial. Each six-minute session was recorded through the Mind Monitor app and labeled correspondingly to the stimuli played.
For the baseline test, the four different channels both dropped leading to a decrease in Power Spectral Density with no brainwaves appearing for delta from 0.5 to 3 Hz and theta frequencies from 3 Hz to 8 Hz. This confirms the accuracy of the results interpreted as delta waves are generated from the deepest form of meditation and dreamless sleep, while theta waves are generated from learning and intuition and can be thought of as generated from beyond a conscious level. We can see an increase in activity from 8-12 Hz which indicates that there is minimal alpha waves activity occurring. Alpha waves are the present resting state of the brain where wave presence indicates relaxation and mental coordination. Frequencies of 12 Hz to 32 Hz are considered beta waves which are indicators of normal brain activity such as having our eyes open and processing information on our surroundings. In regards to our baseline testing, there are some beta waves present in the different channels which can indicate that the test subject was processing the experiment and the environment during testing. The highest activity present in the baseline testing were gamma waves. Gamma waves occur at 32 Hz and above and are considered the fastest waves out of all the aforementioned waves. Gamma waves are associated, and mainly occur, with high levels of thought and focus when conscious. In this baseline test, there were high spikes of gamma waves which indicate most likely that the subject was focused on staying still and clear headed.
With an overlook of the slow commercial music channel spectra, we can see that there is much more activity in PSD and rise in waves for different frequencies. During 3 – 8 Hz, there is no noteworthy activity for theta waves from all channels. This indicates that there was little to no learning or intuitive activity beyond a conscious level during the listening to slow commercial music test. In the 8 – 12 Hz range, there is minimal activity for alpha waves for all channels. This indicates that there was minimal mental coordination and relaxation during testing. For the 12 – 32 Hz frequency range, there is significantly higher PSD and waves from one channel than the others for beta waves. This could indicate that one area of the brain picked up more beta waves than the other channels, which is an indication that there was higher level of concentration on processing the environment they were in. In the 32 Hz and above range, there is significantly higher activity for all channels in regards to gamma waves. This indicates that the slow commercial music resulted in a high level of conscious thought and focus.
At a first look at the classic calm computer-generated music channel spectra, we can see that there is a higher activity in all the channels throughout the frequency range and PSD. During 3 – 8 Hz, there is little activity for theta waves from half of the channels but the PSD does start at a lower value than the commercial music test for all channels. This could indicate that there little learning or intuitive activity beyond a conscious level during the listening to classic calm computer-generated test. In the 8 – 12 Hz range, there is the activity for alpha waves for all channels with some channels slightly dropping in PSD while other channels have alpha waves forming. This indicates that there was minimal mental coordination and relaxation during testing. For the 12 – 32 Hz frequency range, there is a rise in PSD and waves from two channels for the beta waves. The two areas of the brain picked up more beta waves than the other channels.
Specifically, for one of the channels at low Beta (at 12-15 Hz) there is a slight formation of low Beta waves that remained consistent until it reached Beta (15-22 Hz) and higher activity was detected through the appearance of multiple Beta waves. The same can’t be said for the other two channels, where when it reached Beta, there was no increase of beta activity due to no waves appearing and the PSD slowly decaying. This can be interpreted that there was thinking occurring during testing with an awareness of self and focus on surroundings during Beta frequency picked up in two areas of the brain [NHAHealth]. In the 32 Hz and above frequency range, there is significantly higher PSD and wave activity for half the channels that proceeded to all channels when it reached above 50 Hz, in regards to gamma waves. This indicates that the classic calm computer-generated test resulted in a high level of conscious thinking and a high level of processing that is more consistent and organized, due to the firing rate of gamma waves, in all areas of the brain during testing.
In conclusion, this project achieved to explore the efficacy of computer-generated music in comparison to human-composed music among students and examined how different music styles and tempos can relate to changes in physiological signals such as EEG brainwave signals. The computer-generated music had a random pattern of behavior on the brain with the four areas of the brain. With the computer-generated music tests, after analyzing the data acquired, the four different channels were picking up the same signal and matching each other in regards to activity. This happened with human-composed music, as well, but not as often or frequently as during computer- generated music. This gave more solidity to the findings and the interpretation of the graphs. It proved that there was higher and stronger activity during particular frequencies picked up from all areas of the brain that can determine how strongly the subject was feeling. The purpose of this project was to examine if human-composed music and computer-generated music can have the same implications on human emotions and physiological signals. The results proved that there is a difference based on signal activity, PSD, and frequency of waves but more testing and analysis are required to reach a fully-formed opinion.
Our group consists of three members that worked on this research project over the summer. My name is Cynthia Love, serving as the project manager. I am also working alongside Omar Alsuhaibani and Pamela Benitez. The purpose of our group’s project was to explore how different styles, sources, and speeds of music affect physiological signals such as EEG signals from the brain or heart rate.
Some real life applications are to gain the ability to measure emotions, something that has not yet been easily quantifiable. Another application is to optimize an up and coming field of therapy known as music therapy, especially for those with auditory triggers. While music therapy has grown in popularity for its ability to effectively reduce feelings of anxiety and stress, it was unknown if computer-generated music was as effective as music composed by humans. This was another research question we had in mind during our experiments.
Our group hypothesized that there would be a positive correlation between music tempo and beta and gamma brain waves. We hypothesized that fast music would enhance beta and gamma brain waves, while slow music would suppress them. Our last hypothesis is that computer-generated music would be less effective at influencing physiological signals.
Materials – The equipment used for our experiments is listed on the screen. The muse headband was used to take EEG brainwaves, while the empatica wristband was used to record heart rate. The mind monitor app allowed us to process the raw EEG data gathered from the experiments. The E4 connect software was used to process the heart rate data. The Interamusi Software is where we derived most of our music from. It is a website that composes custom music and allows it to be played on a loop.
Testing – this is the type of software that we used for each data processing. The one on the left is the empatica real-time app, which was used to process heart rate data. The app on the right is the mind monitor app, which was used to process EEG data.
Methods – For this experiment, we first started with a baseline test. In this portion, participants were instructed to sit in an empty room, with their eyes closed. Lights were turned off in the room to prevent any electrical interference with the equipment used. The Muse 2 headband was placed onto the participant, as well as the E4 on the participant’s wrist. Both devices were turned on and captured the participant’s readings for 6 minutes. The next portion involved the music tests. Participants listened to each of the 9 computer-generated songs for 6 minutes each. These songs were split into two categories, computer-generated classic music, and computer-generated modern music. There were then three subcategories, known as calm, stimulus, and focused. The order in which the music was played was randomized for every single participant.
For each stimulus, the Muse 2 headband and E4 were activated at the start of each 6-minute trial. Each six-minute session was recorded through the Mind Monitor app and labeled correspondingly to the stimuli played.
For the baseline test, the four different channels both dropped leading to a decrease in Power Spectral Density with no brainwaves appearing for delta from 0.5 to 3 Hz and theta frequencies from 3 Hz to 8 Hz. This confirms the accuracy of the results interpreted as delta waves are generated from the deepest form of meditation and dreamless sleep, while theta waves are generated from learning and intuition and can be thought of as generated from beyond a conscious level. We can see an increase in activity from 8-12 Hz which indicates that there is minimal alpha waves activity occurring. Alpha waves are the present resting state of the brain where wave presence indicates relaxation and mental coordination. Frequencies of 12 Hz to 32 Hz are considered beta waves which are indicators of normal brain activity such as having our eyes open and processing information on our surroundings. In regards to our baseline testing, there are some beta waves present in the different channels which can indicate that the test subject was processing the experiment and the environment during testing. The highest activity present in the baseline testing were gamma waves. Gamma waves occur at 32 Hz and above and are considered the fastest waves out of all the aforementioned waves. Gamma waves are associated, and mainly occur, with high levels of thought and focus when conscious. In this baseline test, there were high spikes of gamma waves which indicate most likely that the subject was focused on staying still and clear headed.
With an overlook of the slow commercial music channel spectra, we can see that there is much more activity in PSD and rise in waves for different frequencies. During 3 – 8 Hz, there is no noteworthy activity for theta waves from all channels. This indicates that there was little to no learning or intuitive activity beyond a conscious level during the listening to slow commercial music test. In the 8 – 12 Hz range, there is minimal activity for alpha waves for all channels. This indicates that there was minimal mental coordination and relaxation during testing. For the 12 – 32 Hz frequency range, there is significantly higher PSD and waves from one channel than the others for beta waves. This could indicate that one area of the brain picked up more beta waves than the other channels, which is an indication that there was higher level of concentration on processing the environment they were in. In the 32 Hz and above range, there is significantly higher activity for all channels in regards to gamma waves. This indicates that the slow commercial music resulted in a high level of conscious thought and focus.
At a first look at the classic calm computer-generated music channel spectra, we can see that there is a higher activity in all the channels throughout the frequency range and PSD. During 3 – 8 Hz, there is little activity for theta waves from half of the channels but the PSD does start at a lower value than the commercial music test for all channels. This could indicate that there little learning or intuitive activity beyond a conscious level during the listening to classic calm computer-generated test. In the 8 – 12 Hz range, there is the activity for alpha waves for all channels with some channels slightly dropping in PSD while other channels have alpha waves forming. This indicates that there was minimal mental coordination and relaxation during testing. For the 12 – 32 Hz frequency range, there is a rise in PSD and waves from two channels for the beta waves. The two areas of the brain picked up more beta waves than the other channels.
Specifically, for one of the channels at low Beta (at 12-15 Hz) there is a slight formation of low Beta waves that remained consistent until it reached Beta (15-22 Hz) and higher activity was detected through the appearance of multiple Beta waves. The same can’t be said for the other two channels, where when it reached Beta, there was no increase of beta activity due to no waves appearing and the PSD slowly decaying. This can be interpreted that there was thinking occurring during testing with an awareness of self and focus on surroundings during Beta frequency picked up in two areas of the brain [NHAHealth]. In the 32 Hz and above frequency range, there is significantly higher PSD and wave activity for half the channels that proceeded to all channels when it reached above 50 Hz, in regards to gamma waves. This indicates that the classic calm computer-generated test resulted in a high level of conscious thinking and a high level of processing that is more consistent and organized, due to the firing rate of gamma waves, in all areas of the brain during testing.
In conclusion, this project achieved to explore the efficacy of computer-generated music in comparison to human-composed music among students and examined how different music styles and tempos can relate to changes in physiological signals such as EEG brainwave signals. The computer-generated music had a random pattern of behavior on the brain with the four areas of the brain. With the computer-generated music tests, after analyzing the data acquired, the four different channels were picking up the same signal and matching each other in regards to activity. This happened with human-composed music, as well, but not as often or frequently as during computer- generated music. This gave more solidity to the findings and the interpretation of the graphs. It proved that there was higher and stronger activity during particular frequencies picked up from all areas of the brain that can determine how strongly the subject was feeling. The purpose of this project was to examine if human-composed music and computer-generated music can have the same implications on human emotions and physiological signals. The results proved that there is a difference based on signal activity, PSD, and frequency of waves but more testing and analysis are required to reach a fully-formed opinion.
One reply on “Music and Emotions”
It is really interesting to know how music is connected to physiological signals. Good findings.