All timeline stories.
Using the Processing environment for Java, a program was created that converts the live EEG data into spectrogram-like images. These images represent a three second sliding window of each bandwidth amplitude at each electrode. This method both visually represents the data, and converts it to a format that a convolutional neural network can understand. See image..Read More
This marks the first successful creation and training of a simple neural network for data-graph classification. The user places dots with a letter value on a canvas, and the network learns the rules of the placement pattern. The network can then generate a later prediction for any position next selected by the user. See image attached…Read More
As a fallback option, if the investigator was not able to actualise the machine learning based system that was envisioned, an alternative setup was created. This alternative system was based on the emotion detection methods used in the “Art of Feeling” project by the studio Random Quark (2017). In principal, a reasonably simple algorithm is applied..Read More
This month, many key documents and papers were read and reviewed. These articles included both practical and theory based research papers, explanatory books, and other literature reviews. These documents provided a solid foundation for the project to be planned from, and confirmed the investigator’s interest in using machine learning for emotion classification. See bibliography list attached…Read More
On this day, presentations of work-in-progress and project concepts were given. A PowerPoint presentation was prepared for this event which detailed all the technical points that had been investigated so far – along with future plans, finance planning and charity goals. See slides attached.
At this point, the book “Nature of Code” was explored. This book was key to the development of the project; it taught the investigator how to replicate natural systems using code, and provoked the idea of using neural networks for emotion classification. This lead to the creation of the first particle system prototype artworks. See attached…Read More
Once the EEG data was accessible, the use of coding for generative art production was explored. Many languages and environments were investigated. However, the most notable were Python, Java and JavaScript. Ultimately, JavaScript was decided to be the most viable, for its powerful drawing capabilities using the P5 library.
The first successful retrieval of data from the EEG headset. Similar integrations had been attempted by the investigator before, but all were in visual coding environments. This was the first time the data had been recorded using just code. Namely, Java in the Processing environment, using the oscP5 library.
This marks the beginning of the project. Initially, the goal was to use electroencephalography and computer programming techniques to create generative art that reflected the emotions felt by a listener, while listening to music.
