Measuring Motivation During Exercise Using EEG

Physical activity is important for our health. It can help improve our physical, mental, and social well-being, and it can also help us live longer. Doing regular Exercise plays a big role in keeping us healthy and preventing diseases like heart issues, diabetes, and brain-related problems. Despite this, many adults do not get enough exercise. The World Health Organization reports that about one in three adults do not meet the recommended physical activity levels. This has led to a goal to reduce physical inactivity by 15% by 2030.

One major reason people struggle to stay active is a lack of Motivation. Motivation influences how we behave, including our willingness to exercise. There are different types of motivation. Some come from within ourselves, known as intrinsic motivation, where we do things because we find them enjoyable. Other types of motivation rely on external factors, such as rewards or social approval. Understanding these types of motivation can help us create strategies to encourage more physical activity.

Measuring motivation accurately, especially during exercise, is essential. Currently, there is no clear method to track motivation levels while people are exercising. Some studies use self-reported surveys to measure motivation, but these can sometimes differ from what people actually do. Other methods look at physical signs, like heart rates, but those can vary based on the type of exercise.

One promising method to measure motivation is through brain activity using a technique called electroencephalography, or EEG. EEG can track electrical activity in the brain in real-time, which is important because motivation is closely related to brain processes. Some EEG signals have been linked to how motivated a person feels during tasks.

The goal of this study was to see if we could measure motivation using EEG while participants were cycling. We wanted to find out if we could detect specific brain responses that relate to motivation during this exercise.

#Study Design

#Participants

The study included 20 healthy individuals, consisting of 6 men and 14 women with an average age of 26. Participants were chosen based on specific criteria, such as age and not having any health issues or conditions that could interfere with exercising. Before participating, they provided written consent and answered questions about their health and physical activity levels.

#Sessions

Each participant took part in two different sessions. In one session, they cycled on a stationary bike. In the other session, they sat on the bike but did not cycle. The order of these sessions was randomized. Before starting, participants completed several questionnaires to evaluate their handedness and physical activity habits.

#Cycling Session

During the cycling session, participants wore a heart rate monitor to track their heart rate throughout the task. First, they rested for five minutes to measure their resting heart rate. Based on that, we calculated a target heart rate for moderate exercise.

Once they reached the target heart rate, participants started cycling at a consistent effort while their brain activity was recorded using EEG. They also completed several trials of a task related to motivation while pedaling.

#Non-Cycling Session

In the non-cycling session, participants sat on the bike and answered the same questions as in the cycling session, but without pedaling. The tasks were the same, allowing for direct comparisons between the two conditions.

#Task Description

#Effort Expenditure for Reward Task (EEfRT)

Participants engaged in a modified task called the Effort Expenditure for Reward Task (EEfRT). The goal of this task was to earn points by completing easy or challenging tasks. The difficulty level was linked to the potential rewards the participants could earn.

They had to choose between an easier task that required less effort and a harder task that could earn them more points. The chances of winning those points varied, which influenced the participants' choices.

#EEG Recording

Participants wore a mobile EEG device equipped with 32 electrodes that captured brain signals as they performed the tasks. The setup allowed us to monitor brain activity while they were cycling and when they were sitting still. The EEG device recorded the electrical activity of the brain in response to different task conditions.

#Data Analysis

After the tasks were completed, we analyzed the EEG data by looking at specific brain waves associated with motivation. The excitement of the brain in response to feedback (like winning or losing points) was measured. We focused on two main brain responses: the P300 and the feedback-related negativity (FRN).

#P300 and FRN

The P300 wave is a brain signal linked to attention and cognitive processing. It typically occurs when a person is surprised by something or when they receive feedback about a task. In this study, we were interested in whether the P300 signal varied based on the exercise condition.

The FRN is another brain response, which indicates how we process rewards or losses. We examined how these responses changed between the cycling and non-cycling sessions to see if cycling affected motivation.

#Results

#EEG Findings

The results showed clear brain responses in both activity conditions. We found that the P300 wave amplitude was different during the cycling session compared to the non-cycling session. The P300 was higher when participants were not cycling, suggesting a change in brain activity based on the physical effort.

In terms of feedback, the P300 amplitude was greater when participants received a reward compared to when they did not. However, the FRN showed a different pattern; it had a lower amplitude when rewards were present than when they were not, indicating that the brain processes findings differently based on whether the outcome was positive or negative.

#Behavioral Findings

The participants' choices during the EEfRT reflected their motivation levels. They were more likely to choose the challenging tasks when the potential rewards were higher or when the chances of receiving a reward were better. This trend was consistent regardless of whether they were cycling or not.

Moreover, participants reported feeling higher levels of exertion as they progressed through the cycling task, but this did not significantly affect their decision-making. The choices made were similar across both sessions, suggesting that cycling did not diminish their motivation to engage in challenging tasks.

#Mood and Perceived Exertion

To assess mood changes, we used a brief mood questionnaire before and after the tasks. However, there were no significant differences in mood between the cycling and non-cycling conditions. Similarly, the perceived exertion levels measured during the tasks increased but did not show differences across the task blocks.

#Discussion

This study provided valuable insights into how we can measure motivation through brain activity during physical exercise. The use of EEG during cycling demonstrated its feasibility for tracking motivation-related brain responses. We found that motivation levels could be linked to specific brain signals, particularly the P300.

The results indicate a shift in cognitive resources when individuals exercise, which ties into theories about how physical activity affects mental processing. While engaging in moderate exercise, brain activity showed changes that align with motivation levels during decision-making tasks.

However, the reduced P300 amplitude during cycling raises questions about how physical exertion influences cognitive engagement. The consistent behavioral choices indicate that exercise may not impact decision-making protocols as much as expected, especially at moderate intensity levels.

Overall, while our findings support the connection between motivation and brain activity, they also suggest that further research is needed to understand the intricate relationship between physical exercise, motivation, and cognitive function.

#Conclusion

This study highlights the potential for EEG to serve as a tool in tracking motivation during physical activity. By demonstrating how brain signals relate to motivation while participants engage in cycling, it opens doors for more studies on the influence of exercise on motivation. Future research could focus on different types of exercise and their effects on motivation, especially in various populations such as those undergoing rehabilitation.

Understanding motivation better can help in designing effective strategies to encourage physical activity, improve health outcomes, and enhance well-being across different age groups and fitness levels.