Have you ever wanted to build a project or research something you are passionate about? STEM is the class that makes that possible. In this course, students work on an independent research project for the first three terms of the year. Projects can focus on a wide range of topics, including biology, physics, math, and engineering. My project explores how sound can influence focus in the brain.
Neurological and focus-related disorders affect millions of people worldwide, making it essential to find easy-to-use treatments. The goal of this project is to develop a simple and accessible way to boost concentration and mental energy. By using specialized "Dynamic" sounds instead of traditional medicine, this research offers a low-cost path to help people reach their peak focus levels. Everyone deserves a fair chance to succeed in school and work, and this project is a step toward making focus-enhancing tools available to everyone.
In today’s digital world, attention span has become significantly shorter due to the increased intake of information. Neuroscience research shows that human focus is closely linked to brainwave activity, with the Beta brainwave zone being associated with attention. However, the precise relationship between external sound frequency and changes in brainwave activity remains unclear. While prior studies demonstrate that sound, music, and auditory feedback can influence arousal and attention, there is a lack of research identifying how changing sound frequencies affect brainwave patterns associated with focus. This project investigates how external auditory stimuli influences brainwave activity. By focusing on changes in alpha (8–12 Hz) and beta (13–30 Hz) frequencies. Using a 16-channel OpenBCI Cyton Daisy EEG brain cap, frontal brain activity was recorded while participants listened to different sounds while performing a cognitive test. The first sound was silence, which was used as a baseline. The second sound was 30Hz, which has shown evidence that it improves focus. The last sound is a dynamically changing sound, that increases or decreases based on the Alpha and Beta power of the participant. Code was written in Python to transform EEG signals into frequency domain using spectral analysis and quantify their alpha and beta power spectral density. This allows for analysis of the changes of Beta and Alpha power spectral. This research addresses a key gap in understanding how specific and changing sound frequencies change focus related brain activity. The findings support the potential for sound-based solutions and adaptive neurofeedback systems to improve attention.
How do external stimuli, such as sound, affect brain waves and overall focus in the brain?
As sound frequency levels change, human focus levels will also change, leading to measurable differences in alpha and beta brainwave activity.
Changing Human Focus Levels by Altering Brain Wave Activity Through Sound The Modern Problem of Focus Declining Attention in a Digital World In today’s modern world, maintaining focus and having long attention spans have become increasingly difficult due to the increased intake of information. An article by Live Science explains how modern technology continuously overwhelms individuals with stimulus like social media, pop up ads and even digital billboards. This overloads our cognitive processing systems and decreases attention span. When the brain is overloaded, stress increases leading to more frequency errors and slower performance (Cytowic, 2024). The Yerks-Dodson Law The Yerks-Dodson Law provides a framework for why performance decreases as information and stress increases. According to the law, human performance can be shown through an upside-down “U” shape, where arousal controls someone’s performance (Pietrangelo, 2020). Performance improves with moderate amounts of excitment but decreases when arousal levels get too high or low. Large amounts of stress can lead to overstimulation and distractions while too little arousal can result in drowsiness and low motivation. The optimal performance point is found when the arousal level reaches the right amount. Brainwave Frequencies Human brain activity can be categorized into five different frequency bands: Delta (0.5 – 4hz), Theta (4 – 8hz), Alpha (8 – 12hz), Beta (12 – 30hz), and Gamma (30 – 100hz). The brain works in a way where increased arousal levels relate to increased frequency. For example, when someone is sleeping, the brain activity is usually in the Delta or Theta frequencies, meaning their arousal is low. However, when a person is going through mental or physical effort, the brain usually moves to the Beta or Gamma zones, meaning arousal is high. Optimal performance is assumed to happen when the brain is in the Beta range as it is a frequency that is connected to alertness but not overstimulation (Deshmukh, 2023). Brainwave Adaptation and Neurofeedback A technique that many researchers use to read brainwaves is through electroencephalogram or EEG. The EEG graphs the electrical activity of the brain to read brain signals and where they come from. It also measures the strength of each brainwave frequency in real time. This technique was used in a study in which participants were told to try and control the frontal theta wave (Kerick et al., 2023). They did this by letting participants see their frontal theta wave data in real-time and telling them to do whatever it takes to increase it. Participants then took a cognitive test where their reaction time and accuracy were tracked. The results showed improvement in both brain activity and behavior, meaning that it’s possible to guide the brain towards a more focused state by changing brainwave activity. External Sound Stimuli as Modulators of Brainwaves ASMR and Sensory Responses In recent years, a new type of online video has gained recognition, called the autonomous-sensory-meridian-response or ASMR videos. These videos are made up of a combination of sounds, images, colors, and trigger words to create relaxation or tingling sensations. In an experiment about how they affect arousal, participants watched 2 different types of ASMR videos (Kim et al., 2024). The first type of video was high valence–low relaxation (HVLR), which were videos with louder sounds, brighter colors, and more things happening on screen. The other type was low valence–high relaxation (LVHR), which were videos with dimmer colors, quieter sounds and more relaxation. They found that HVLR videos increased alertness and Beta brainwave activity. LVHR, on the other hand, invoked delta waves and increased relaxation. This shows that sound and visuals together can influence arousal and brain activity. Music and Brainwave Synchronization The use of music is another stimulus that can affect focus levels. A study tested how different genres of music affected Alpha brainwaves, which are linked to relaxation (Tai and Kuo, 2019). In the study, multiple types of songs were played to participants. The songs were categorized into happy, dark, fast and slow. EEG was collected while different categories were played. In the end, they found that fast, energetic music lowered alpha activity and increased beta and gamma activity meaning it made people more alert. Slow, clam music increased alpha activity, helping people relax. This suggests that intensity of music and sound in general can change how focused or calm someone feels. Lowering Arousal to Improve Performance A brain computer interface or BCI is a system that allows direct communication between the brain and external devices without going through the body’s muscles and nerves. It works by collecting brain signals and processing them to try and complete a task. In a study by Faller et al. (2019), a BCI was used to find out how sound feedback based on brain activity could help people control arousal during stressful tasks. They tested this with a flight simulation where participants heard sounds generated by a BCI that reacted to their EEG readings. The objective of the BCI noise was to lower arousal levels. In the study, participants would either listen to BCI, silence, or random noises, while they had to guide a plane through rings in virtual reality. The scientists found that those who used the BCI generated sound performed better and stayed focused longer than those who heard random noise or silence. This suggests that sound can directly be connected to brain activity and that different sound patterns can control arousal to improve performance in demanding tasks. Synthesis of Prior Findings Across all the many studies reviewed, researchers agree that sound has repeatedly been shown to influence brain activity, arousal, and focus. ASMR studies showed that certain types of sound and visual combinations can shift the brain between relaxed (delta and alpha waves) and alert states (beta waves) (Kim et al., 2024). Music experiments also demonstrated that fast, energetic songs increase beta and gamma activity while calm music increases alpha waves. This shows that sound intensity and frequency can directly change how alert or relaxed someone feels (Tai and Kuo, 2019). Binaural beat research also supports the idea that sound influences brain activities. Finally, research using brain-computer interfaces (BCIs) shows that sound can help people control arousal in real time (Faller et al., 2019). This supports the Yerkes-Dodson Law by showing that sound can be used to lower arousal and reach an optimal performance level. Conclusion Although prior research demonstrates connections between sound, arousal, and focus, they don’t define the exact relationship between sound frequency and brainwave activity for focus. It is still not known how changing sound frequencies affect focus. This project aims to fill this gap by using EEG data to test how changing the hertz of sound frequency while performing a task can affect brain activity. By finding the best frequences to change too, this research hopes to show how sounds can be used to improve attention and performance.
The methodology utilized a quantitative, experimental approach to measure the impact of varying auditory frequencies on cognitive performance and neurological arousal among a cohort of 11 participants. Participants completed the Paced Auditory Serial Addition Task (PASAT) under three distinct environments: a 0Hz silence control, a 30Hz fixed tone, and a "Dynamic" condition featuring variable frequency triggers. Throughout the task, focus was monitored using an EEG headset to isolate Beta waves (13–30 Hz) and Alpha waves (8–12 Hz), which were used to calculate the Beta/Alpha ratio. To identify the specific "Optimal Focus Zone," the data was further analyzed in 5Hz bins across the 20–50Hz spectrum, with statistical significance determined through a One-Way ANOVA and Tukey’s HSD post-hoc test.
The analysis of this study utilized a multi-layered statistical approach to evaluate both behavioral performance and neurological state. For behavioral metrics, a paired-samples t-test was conducted to compare reaction times between the 0Hz control and the Dynamic condition. This test was selected to determine if the variable-pitch triggers produced a statistically significant change in processing speed for the same group of participants. For task accuracy, descriptive statistics were calculated to monitor for any performance degradation. The primary neurological metric was the Beta/Alpha ratio, used as a quantitative proxy for cortical engagement. By dividing the power of high-frequency Beta waves (focus) by low-frequency Alpha waves (relaxation), we created a normalized arousal index for each participant. To compare these ratios across the three auditory environments, a One-Way ANOVA was employed. Upon finding global significance, a Tukey HSD (Honestly Significant Difference) post-hoc test was used for pairwise comparisons. This allowed for the rigorous identification of the 20–25Hz "Optimal Focus Zone" by isolating it from other frequency bins while controlling for Type I error across multiple comparisons.
The objective of this study was to investigate how external auditory stimuli influences focus and brainwave activity and to also determine if a closed loop adaptive system could enhance cognitive performance. Overall, the objectives were accomplished. The data demonstrated that sound frequency significantly changed neurological arousal, and a dynamically adjusting auditory stimulus produced stronger effects than a fixed frequency tone. The first hypothesis was “As sound frequency levels change, human focus levels will also change, leading to measurable differences in alpha and beta brainwave activity." This hypothesis was supported by the significant differences in the Beta/Alpha ratio when listening to silence, 30Hz and dynamically changing sounds. The Dynamic conditions produced a statistically significant increase in Beta/Alpha compared to both the 0Hz control (p = 0.006) and the 30Hz fixed tone (p = 0.0084). A T test was used for this. Additionally, analysis across frequency ranges (20 – 50 Hz) revealed a significant decline in Beta/Alpha ratio as pitch increased (ANOVA, p less than 0.0001). These findings confirm that altering sound frequency changes measurable brainwave activity. The second hypothesis is “Exposure to higher sound frequencies will be associated with increased beta band power and decreased alpha band power” This hypothesis was partially supported, and the findings were the reverse of what was expected. While lower frequencies (20 – 25Hz) were associated with the highest Beta/Alpha rations, higher frequencies (40 – 50 Hz) were associated with significantly lower arousal levels. This suggests that the relationship between pitch and focus is not linear and that optimal performance is at moderate or lower frequencies rather than at extremes. Reaction time significantly improved during the Dynamic condition (p = 0.0089), showing that neurological changes match changes in cognitive behavior. However, task accuracy did not significantly change between conditions suggest that auditory modulation primarily enhanced speed rather than correctness. This could also be an effect on the length of the test as only 40 questions were answered as part of the PASAT test. If there were more questions answer, there would be more data to verify the results. ADHD participants (n = 2) were also tested on to see if they exhibited greater neurological results compared to non-ADHD participants (n = 9). The findings show p = 0.06 which is not quite significant but show that the results are very close to being significant. If tested on a larger sample, there would be a better picture on how ADHD is affected by sound frequencies. Paired Student’s T-tests were used to compare subject differences because participants ran the repeated tests with each participant experiencing all conditions. One-Way ANOVA was also used for frequency range comparisons as more than two groups were analyzed simultaneously. Tukey’s HSD post-hoc testing was used to control Type I error and statistical significance was classified as alpha = 0.05. Some limitations encountered were a small sample size of 11. This was even smaller in the ADHD subgroup with a size of 2. Recording was also short and was limited to 2 minutes per conditions that does not account for long term fatigue. There was also the factor for environmental noise but was minimized with a Faraday cage. The last limitation was very mixed age and gender groups which could create variability in EEG patterns. The findings of this study align with and extend prior research that examined the relationship between sound, arousal, and brainwave activity. Previous music-based studies demonstrated that different types of music could increase Beta activity and decrease alpha activity which would increase alertness (Tai and Kuo, 2019). Similarly, ASMR research showed that variations in stimulus s can shift individuals between relaxed and alert states (Kim et al., 2024). Neurofeedback research by Faller et al. (2019) also demonstrated that sound that changed based off brain activity can improve performance in cognitive tasks by regulating arousal. This study builds on those findings by isolating pure sound frequencies rather than complex music and it implemented a real time closed loop system based off brain activity. This project advances the field by directly quantifying Beta/Alpha ratios during cognitive tasks and dynamically adjusting sound to maintain optimal arousal. It puts together different brain entrainment research with adaptive neurofeedback technology to create a more personalized approach to increasing focus. Future Research Future studies would expand upon these findings by increasing the sample size to improve statistical power and validate my ADHD trends. Longer experimental sessions would also help determine whether the observed neural enhancements persisted over time or diminish. Additionally, future studies could examine other EEG frequency bands, such as theta and gamma, to explore potential effects on memory and executive function. The study could also compare Dynamic sounds to binaural beats or music. Finally, applying the closed loop system in real world environments would help determine its practical applications. This research is important because it moves towards the development of personalized risk-free tools that increase cognitive ability for all. This study investigated how external auditory stimuli influences brainwave activity and cognitive focus. Using a 16-channel EEG system, participants completed a standardized cognitive task while exposed to silence, a fixed 30Hz tone, and a dynamically adapting sound frequency. Brainwave activity was quantified using spectral analysis of alpha and beta frequency bands, and behavioral performance was measured through reaction time and accuracy. The results demonstrated that sound frequency significantly affects neurological arousal. The Dynamic closed-loop condition produced the highest Beta/Alpha ratios and significantly reduced reaction time compared to silence. Additionally, the 20–25 Hz range produced the strongest neural arousal, suggesting the presence of an optimal auditory frequency band for focus enhancement. While task accuracy did not significantly change, faster reaction times indicate improved cognitive efficiency. These findings support the hypothesis that external sound frequency can modulate brainwave activity and influence focus. Furthermore, the success of the adaptive neurofeedback system suggests that personalized, real-time auditory modulation may be more effective than static stimulation. In a world increasingly filled with distractions and cognitive overload, the ability to scientifically tune focus through sound presents powerful implications. This research contributes to the growing field of neuroadaptive technology by demonstrating that attention is not only measurable, but modifiable.