Exploring the Mental State Intersection by Brain-Computer Interfaces, Cellular Automata and Biofeedback

Brain-Computer Interfaces (BCI) allows systems to be controlled by signals derived from Electroencephalogram (EEG) analysis. Several low-cost electroencephalographs are available on the market that provides high-quality EEG signals. A very interesting approach in this domain is to represent a user's mental state by using an EEG signal. In this paper, we propose a method to represent and describe the user's mental state by exploiting Cellular Automata (CA). Using BCI, CA, and Autoencoder Neural Network we provide a graphical description and audible representation of the current mental state of the user wearing the device. We demonstrated experimentally the capability of generating a representation of the mental states, both CA and sound texture, while at the same time can be exploited as a biofeedback approach.