All timeline stories.
The investigator begins researching ways of artistically representing the data generated by the Neural Scores application. During this time, many programming environments were explored. However, the investigator decided to build the frontend system in TouchDesigner with Python. This framework was chosen for its powerful shader-based visual rendering capabilities.
In accordance with the university’s policies surrounding participant-based research, an ethics application form was completed. This document also includes the participant information and consent forms, and the risk assessment. The form was submitted to the project supervisor and approved by them after review. See document attached.
As part of the ongoing research conducted by the Creative AI Lab, founded by Bunz and Jager, a call for case studies was put out by Serpentine Galleries. In this posting, the investigators requested that artists who work with artificial intelligence provide them with images and explanations of the internal tooling of their systems. See images..Read More
In coding research, the investigator discovered the Electron.js framework for Node.js JavaScript. This framework allows JavaScript code, which would typically only work in internet browsers, to run in standalone desktop applications through Node.js. Upon learning this, the investigator began working on building the emotion detection system into this framework.
Using the Electron.js framework, the investigator rebuilt the emotion classification network as a standalone desktop application that could be installed easily. At this point, the emotion graph featured previously was also redesigned to a circumplex graph superimposed over an emotion colour wheel. This is a further exploration of the circumplex model of affect. See attached images…Read More
At this point, the initial project supervisor announced their plans to go on research leave for their post-doc studies. A new supervisor was assigned to the project, and presentations were given to bring them up to date. See slides attached.
The network UI was redesigned to include a very simple graph that exhibited the current emotion of the participant. This was based on the circumplex model of affect, as suggested by Posner et al (2005). The model was also made to broadcast the values it generates over OSC, to be used in other applications. See video..Read More
A monumental milestone in the project. The first emotion detection network was created, which used the same technique as the blink detection network. To train the model, EEG data was recorded while the investigator was listening to music that was thought to provoke states of high activation, low activation, high valence, and low valence. See attached..Read More
A model that had the ability to predict when a participant’s eyes were closed was created. This was done using a convolutional neural network and the EEG ‘spectrogram’ system created earlier. The EEG spectrogram program was also rewritten to run in JavaScript, rather than Java. The model accuracy confirmed the spectrogram approach was viable. See attached..Read More
The investigator liaised with organisers of the Mind mental health charity, to negotiate a fundraiser event to take place during the project exhibition in July. This would be used as an opportunity to receive charitable donations, in exchange for material goods produced during the project (i.e., artwork prints, artwork booklets, etc.).
