Evolutionary art with an EEG fitness function

This project involved the use of an interactive Genetic Al-gorithm (iGA) with an electroencephalogram (EEG)-based fitness function to create paintings in the style of Piet Mondrian, a Dutch painter who used geometric elements in his later paintings. Primary data for the prototype was gathered by analysis of twenty-seven existing Mondrian paintings. An EEG gaming headset was used to read EEG signals, which were transmitted by Bluetooth to an Arduino running an iGA. These values were used as the iGA fitness function. The data was sent to a PC running Processing to dis-play the artwork. The resultant displayed artwork evolves to favour higher attention and meditation levels, which are considered to represent greater mindfulness. The process ends when the observer identifies a piece of art they would like to keep. However, convergence of the algorithm is difficult to test as many parameters can affect the process. A number of issues aris-ing from the research are discussed and further work is proposed

Evolutionary art with an EEG fitness function

This project involved the use of an interactive Genetic Al-gorithm (iGA) with an electroencephalogram (EEG)-based fitness function to create paintings in the style of Piet Mondrian, a Dutch painter who used geometric elements in his later paintings. Primary data for the prototype was gathered by analysis of twenty-seven existing Mondrian paintings. An EEG gaming headset was used to read EEG signals, which were transmitted by Bluetooth to an Arduino running an iGA. These values were used as the iGA fitness function. The data was sent to a PC running Processing to dis-play the artwork. The resultant displayed artwork evolves to favour higher attention and meditation levels, which are considered to represent greater mindfulness. The process ends when the observer identifies a piece of art they would like to keep. However, convergence of the algorithm is difficult to test as many parameters can affect the process. A number of issues aris-ing from the research are discussed and further work is proposed

A first attempt at constructing genetic programming expressions for EEG classification

Proceeding of: 15th International Conference on Artificial Neural Networks ICANN 2005, Poland, 11-15 September, 2005In BCI (Brain Computer Interface) research, the classification of EEG signals is a domain where raw data has to undergo some preprocessing, so that the right attributes for classification are obtained. Several transformational techniques have been used for this purpose: Principal Component Analysis, the Adaptive Autoregressive Model, FFT or Wavelet Transforms, etc. However, it would be useful to automatically build significant attributes appropriate for each particular problem. In this paper, we use Genetic Programming to evolve projections that translate EEG data into a new vectorial space (coordinates of this space being the new attributes), where projected data can be more easily classified. Although our method is applied here in a straightforward way to check for feasibility, it has achieved reasonable classification results that are comparable to those obtained by other state of the art algorithms. In the future, we expect that by choosing carefully primitive functions, Genetic Programming will be able to give original results that cannot be matched by other machine learning classification algorithms.Publicad

Multi-objective particle swarm optimization for channel selection in brain-computer interfaces

This paper presents a novel application of a multi-objective particle swarm optimization (MOPSO) method to solve the problem of effective channel selection for Brain-Computer Interface (BCI) systems. The proposed method is tested on 6 subjects and compared to another search based method, Sequential Floating Forward Search (SFFS). The results demonstrate the effectiveness of MOPSO in selecting a fewer number of channels with insignificant sacrifice in accuracy, which is very important to build robust online BCI systems

Mindfulness mirror

This paper explores the use of an interactive Genetic Algorithm for creating a piece of visual art intended to assist in promoting the state of mindfulness. This is determined by a Bluetooth gaming electroencephalography (EEG) headset as the fitness function. The visual display consisted of an infinity mirror with over two hundred Neopixels with fade times and colour of zones controlled by two Ardu-inos running the software. Whilst we have observed some convergence of solu-tions, the results and user observations raised some interesting questions about how this strategy might be improved

Using Genetic Algorithms for Automatic Recurrent ANN Development: an Application to EEG Signal Classification

[Abstract] ANNs are one of the most successful learning systems. For this reason, many techniques have been published that allow the obtaining of feed-forward networks. However, fe w works describe techniques for developing recurrent networks. This work uses a genetic algorithm for automatic recurrent ANN devel opment. This system has been applied to solve a well-known problem: classi fication of EEG signals from epileptic patients. Results show the high performance of this system, and its ability to develop simple networks, with a low number of neurons and connections.Red Gallega de Investigación sobre Cáncer Colorrectal; ref. 2009/58Programa Ibeoramericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-53Xunta de Galicia; 10SIN105004PRInstituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/000