Search

Compared to the different brain signals used in brain–computer interface (BCI) paradigms, the s teady-state visually evoked potential (SSVEP) features a high signal to noise ratio, enabling reliable and fast classification of neural activity patterns without extensive training requirements. In this paper, we present methods to further increase the information transfer rates (ITRs) of SSVEP-based BCIs. Starting with stimulus parameter optimizations methods, we develop an improved approach for the use of Canonical correlation analysis and analyze properties of the SSVEP when the user fixates a target and during transitions between targets. These transitions show a negative effect on the system's ITR which we trace back to delays and dead times of the SSVEP. Using two classifier types adapted to continuous and transient SSVEPs and two control modes (fast feedback and fast input), we present a simulated online BCI implementation which addresses the challenges introduced by transient SSVEPs. The resulting system reaches an average ITR of 181 Bits/min and peak ITR values of up to 295 Bits/min for individual users. [ABSTRACT FROM AUTHOR]

When people speak about consciousness, they distinguish various types and different levels, and they argue for different concepts of cognition. This complicates the discussion about artificial or machine consciousness. Here we take a bottom-up approach to this question by presenting a family of robot experiments that invite us to think about consciousness in the context of artificial agents. The experiments are based on a computational model of sensorimotor contingencies. It has been suggested that these regularities in the sensorimotor flow of an agent can explain raw feels and perceptual consciousness in biological agents. We discuss the validity of the model with respect to sensorimotor contingency theory and consider whether a robot that is controlled by knowledge of its sensorimotor contingencies could have any form of consciousness. We propose that consciousness does not require higher-order thought or higher-order representations. Rather, we argue that consciousness starts when (i) an agent actively (endogenously triggered) uses its knowledge of sensorimotor contingencies to issue predictions and (ii) when it deploys this capability to structure subsequent action. [ABSTRACT FROM AUTHOR]

Modulation of steady-state visual evoked potential (SSVEP) by directing gaze to targets flickering at different frequencies has been utilized in many brain-computer interface (BCI) studies. However, this paradigm may not work with patients suffering from complete locked-in syndrome or other severe motor disabilities that do not allow conscious control of gaze direction. In this paper, we present a novel, independent BCI paradigm based on covert shift of non-spatial visual selective attention. Subjects viewed a display consisting of two spatially overlapping sets of randomly positioned dots. The two dot sets differed in color, motion and flickering frequency. Two types of motion, rotation and linear motion, were investigated. Both, the SSVEP amplitude and phase response were modulated by selectively attending to one of the two dot sets. Offline analysis revealed a predicted online classification accuracy of 69.3+/-10.2% for the rotating dots, and 80.7+/-10.4% for the linearly moving dots.

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents.