Abstract
We propose a brain-computer interface (BCI) system for evolving images in real-time based on subject feedback derived from electroencephalography (EEG). The goal of this system is to produce a picture best resembling a subject’s ‘imagined’ image. This system evolves images using Compositional Pattern Producing Networks (CPPNs) via the NeuroEvolution of Augmenting Topologies (NEAT) genetic algorithm. Fitness values for NEAT-based evolution are derived from a real-time EEG classifier as images are presented using rapid serial visual presentation (RSVP). Here, we report the design and performance, for a pilot training session, of a BCI system for real-time single-trial binary classification of viewed images based on participant-specific brain response signatures present in 128-channel EEG data. Selected training-session image clips created by the image evolution algorithm were presented in 2-s bursts at 8/s. The subject indicated by subsequent button press whether or not each burst included an image resembling two eyes. Approximately half the bursts included such an image. Independent component analysis (ICA) was used to extract a set of maximally independent EEG source time-courses and their 100 minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from the (94%) bursts followed by correct manual responses. To estimate the likelihood that the post-image EEG contained EEG ‘flickers’ of target recognition, we applied two Fisher discriminant classifiers to the time and/or time-frequency features. The area under the receiver operating characteristic (ROC) curve by tenfold cross-validation was 0.96 using time-domain features, 0.97 using time-frequency domain features, and 0.98 using both domain features.
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Basa, T., Go, C., Yoo, K., Lee, W.: Using Physiological Signals to Evolve Art. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 633–641. Springer, Heidelberg (2006)
Bell, A., Sejnowski, T.: An Information Maximization Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Bigdely-Shamlo, N., Vankov, A., Ramirez, R., Makeig, S.: Brain Activity-Based Image Classification From Rapid Serial visual Presentation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 16(4) (2008)
Cavanna, A.E., Trimble, M.R.: The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564–583 (2006)
Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. of Neuroscience Methods 134(1), 9, http://sccn.ucsd.edu/eeglab
DelphiNEAT GeneticArt program, http://www.mattiasfagerlund.com/DelphiNEAT/
Einhuser, W., Mundhenk, T.N., Baldi, P., Koch, C., Itti, L.: A bottom-up model of spatial attention predicts human error patterns in rapid scene recognition. J. of Vision 7(10), 1–13 (2007)
Gerson, A., Parra, L., Sajda, P.: Cortically-coupled computer vision for rapid image search. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2), 174–179 (2006)
Lee, T., Girolami, M., Sejnowski, T.J.: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources. Neural Computation 11(2), 417–441 (1999)
Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.: Independent component analysis of electroencephalographic data. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 145–151 (1996)
Makeig, S., Enghoff, S., Jung, T.P., Sejnowski, T.J.: A natural basis for efficient brain-actuated control. IEEE Trans. Rehabil. Eng. 8, 208–211 (2000)
Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D., Sejnowski, T.J.: Blind separation of auditory event-related brain responses into independent components. Proc. Natl. Acad. Sci. USA 94, 10979–10984 (1997)
Parra, L.C., Christoforou, C., Gerson, A.D., Dyrholm, M., Luo, A., Wagner, M., Philiastides, M.G., Sajda, P.: Spatio-temporal linear decoding of brain state: Application to performance augmentation in high-throughput tasks. IEEE Signal Processing Magazine 25(1), 95–115 (2008)
Sajda, P., Gerson, A., Parra, L.: High-throughput image search via single-trial event detection in a rapid serial visual presentation task. In: Proc. 1st Inter. IEEE EMBS Conf. on Neural Engineering, Capri Island, Italy (2003)
Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Campbell, A., Stanley, K.O.: Picbreeder: Evolving Pictures Collaboratively Online. In: Proc. Computer Human Interaction Conf (CHI), 10 Pages. ACM Press, New York (2008)
Stanley, K.O.: Evolving Neural Networks through Augmenting Topologies. Evolutionary Computation 10(2), 99 (2002)
Stanley, K.O.: Exploiting Regularity Without Development. In: Proc. AAAI Fall Symposium on Developmental Systems, 8 pages. AAAI Press, Menlo Park (2006)
Talairach Client, http://www.talairach.org/client.html
Worden, M.S., Foxe, J.J., Wang, N., Simpson, G.V.: Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J. Neurosci. 20, RC63 (2000)
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Bigdely Shamlo, N., Makeig, S. (2009). Mind-Mirror: EEG-Guided Image Evolution. In: Jacko, J.A. (eds) Human-Computer Interaction. Novel Interaction Methods and Techniques. HCI 2009. Lecture Notes in Computer Science, vol 5611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02577-8_62
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DOI: https://doi.org/10.1007/978-3-642-02577-8_62
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