Particle Simulation Breakdown

This is a simple particle motion simulation with collision sounds. The particles are created using JavaScript and the canvas element. The particles are given a random velocity and direction and are then updated every frame. When a particle collides with the edge of the canvas, it plays a collision sound. The number of particles and the speed of the simulation can be adjusted using the controls.


Overview of the Web Application

This web application is an interactive particle simulation designed to demonstrate physics principles and sound synthesis in a visually engaging manner. The application utilizes HTML5, CSS, and JavaScript, with the core functionality embedded within a JavaScript file.

At the heart of the application are two main classes: StationaryCircle and Particle. StationaryCircle creates static circles, used here to define the central stationary circle and the boundary circle. The Particle class represents the moving elements within these circles, each moving with a set speed and colliding with the boundaries and each other.

Key Features:

How to Use the Application

To use this web application, you’ll interact with a canvas displaying moving particles within a circular boundary. Adjust the number of particles and their speed using the provided sliders. As the particles move and collide, they generate sounds based on their velocities and predefined frequencies. If you want to reset the simulation with new settings, just hit the "Restart Simulation" button. This application is user-friendly and requires no advanced knowledge to interact with and enjoy the physics and sound synthesis demonstration.

Future Directions: EEG Data Visualization and Analysis

Envisioning future directions, this particle simulation framework could be adapted for more complex and meaningful applications, such as visualizing and analyzing EEG (Electroencephalography) data, particularly in the context of neurological conditions like epilepsy.

For instance, imagine using a similar particle system where each particle represents a different EEG data point or channel. Movements and collisions of particles could correspond to different brainwave patterns, with the color or size of particles indicating abnormalities like epileptic spikes or seizures. This could provide a dynamic, real-time representation of brain activity, making it easier for researchers and clinicians to identify and analyze patterns associated with epileptic seizures.

Moreover, integrating sound synthesis could add another dimension to the analysis. Different frequencies or sound patterns could be linked to specific types of brainwaves or seizure activities, providing an auditory cue to complement the visual representation. This multisensory approach could enhance the understanding and interpretation of complex EEG data, offering a novel tool for diagnosis, research, and even patient education about their neurological conditions.


This Particle Motion Simulation tool was developed with assistance from AI technologies, including OpenAI's GPT-4 and GitHub Copilot. These AI systems provided guidance and suggestions during the coding process. However, it's important to note that while AI contributes significantly, the final implementation, design decisions, and responsibility for the code's functionality and performance lie with the human developer. The AI systems are tools to augment the development process, not replacements for human creativity and decision-making.