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References
Please acknowledge the work of the SmartphoneBrainScanner2 by citing (Stopczynski et al, 2011):
A. Stopczynski, J. E. Larsen, C. Stahlhut, M. K. Petersen, & L. K. Hansen (2011), A smartphone interface for a wireless EEG headset with real-time 3D reconstruction. Affective Computing and Intelligent Interaction (ACII 2011), Lecture Notes in Computer Science (LNCS) 6357, Springer-Verlag Berlin Heidelberg, pp.317-318.
Abstract We demonstrate a fully functional handheld brain scanner consisting of a low-cost 14-channel EEG headset with a wireless connection to a smartphone, enabling minimally invasive EEG monitoring in naturalistic settings. The smartphone provides a touch-based interface with real-time brain state decoding and 3D reconstruction.
M. K. Petersen, C. Stahlhut, A. Stopczynski, J. E. Larsen, & L. K. Hansen (2011), Smartphones get emotional: mind reading images and reconstructing the neural sources, 1st workshop on machine learning for affective computing (MLAC) at the Affective Computing and Intelligent Interaction (ACII 2011), Lecture Notes in Computer Science (LNCS) 6357, Springer-Verlag Berlin Heidelberg, pp.578-587.
Abstract Combining a wireless EEG headset with a smartphone offers new opportunities to capture brain imaging data reflecting our everyday social behavior in a mobile context. However processing the data on a portable device will require novel approaches to analyze and interpret significant patterns in order to make them available for runtime interaction. Applying a Bayesian approach to reconstruct the neural sources we demonstrate the ability to distinguish among emotional responses reflected in different scalp potentials when viewing pleasant and unpleasant pictures compared to neutral content. Rendering the activations in a 3D brain model on a smartphone may not only facilitate differentiation of emotional responses but also provide an intuitive interface for touch based interaction, allowing for both modeling the mental state of users as well as providing a basis for novel bio-feedback applications.
J. E. Larsen, A. Stopczynski, C. Stahlhut, M. K. Petersen, & L. K. Hansen (2012), A cross-platform smartphone brain scanner. CHI 2012 Workshop on Personal Informatics in Practice: Improving Quality of Life Through Data, pp.1-4.
Abstract We describe a smartphone brain scanner with a low-cost wireless 14-channel Emotiv EEG neuroheadset interfacing with multiple mobile devices. This personal informatics system enables minimally invasive and continuous capturing of brain imaging data in natural settings. The system applies an inverse Bayesian framework to spatially visualize the activation of neural sources real-time in a 3D brain model or to visualize the power of brainwaves within specic frequencies. We describe the architecture of the system and discuss initial experiments.
C. Stahlhut, H. T. Attias, A. Stopczynski, M. K. Petersen, J. E. Larsen, & L. K. Hansen (2012), An evaluation of EEG scanner's dependence on the imaging technique, forward model computation method, and array dimensionality, 34th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
Abstract EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup’s dependence on correct forward modeling. Specifically, we examine how different forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning.