Showing papers by "Patrick J. Loughlin published in 2014"

Assessing vibrotactile feedback strategies by controlling a cursor with unstable dynamics

Abstract: Brain computer interface (BCI) control predominately uses visual feedback. Real arm movements, however, are controlled under a diversity of feedback mechanisms. The lack of additional BCI feedback modalities forces users to maintain visual contact while performing tasks. Such stringent requirements result in poor BCI control during tasks that inherently lack visual feedback, such as grasping, or when visual attention is diverted. Using a modified version of the Critical Tracking Task [1] which we call the Critical Stability Task (CST), we tested the ability of 9 human subjects to control an unstable system using either free arm movements or pinch force. The subjects were provided either visual feedback, ‘proportional’ vibrotactile feedback, or ‘on-off’ vibrotactile feedback about the state of the unstable system. We increased the difficulty of the control task by making the virtual system more unstable. We judged the effectiveness of a particular form of feedback as the maximal instability the system could reach before the subject lost control of it. We found three main results. First, subjects can use solely vibrotactile feedback to control an unstable system, although control was better using visual feedback. Second, ‘proportional’ vibrotactile feedback provided slightly better control than ‘on-off’ vibrotactile feedback. Third, there was large intra-subject variability in terms of the most effective input and feedback methods. This highlights the need to tailor the input and feedback methods to the subject when a high degree of control is desired. Our new task can provide a complement to traditional center-out paradigms to help boost the real-world relevance of BCI research in the lab.

Time-frequency filtering for classifying targets in nonstationary clutter

Abstract: Classifying underwater targets from their sonar backscatter is often complicated by induced or self-noise (i.e. clutter, reverberation) arising from the scattering of the sonar pulse from non-target objects. Because clutter is inherently nonstationary, and because the propagation environment can induce nonstationarities as well, in addition to any nonstationarities / time-varying spectral components of the target echo itself, a joint phase space approach to target classification has been explored. In this paper, we apply a previously developed minimum mean square time-frequency spectral estimation method to design a bank of time-frequency filters from training data to distinguish targets from clutter. The method is implemented in the ambiguity domain in order to reduce computational requirements. In this domain, the optimal filter (more commonly called a “kernel” in the time-frequency literature) multiples the ambiguity function of the received signal, and then the mean squared distance to each target class is computed. Simulations demonstrate that the class-specific optimal kernel better separates each target from the clutter and other targets, compared to a simple mean-squared distance measure with no kernel processing.

Instantaneous frequency and the Wigner-Gabor signal

Abstract: Determining the amplitude and phase of a signal is important in many areas of science and engineering. The derivative of the phase is typically called the "instantaneous frequency," which in principle mathematically describes (and ideally coincides with) the common physical experiences of variable-frequency phenomena, such as a siren. However, there is an infinite number of different amplitude-phase pairs that will all generate the same real signal, and hence there is an unlimited number of "instantaneous frequencies" for a given real signal. Gabor gave a procedure for associating a specific complex signal to a given real signal, from which a unique definition of the amplitude and phase, and consequently the instantaneous frequency, of the real signal is obtained. This complex signal, called the analytic signal, is obtained by inverting the Fourier spectrum of the real signal over the positive frequency range only. We introduce a new complex signal representation by applying Gabor's idea to the Wigner time-frequency distribution. The resulting complex signal, which we call the Wigner-Gabor signal, has a number of interesting properties that we discuss and compare with the analytic signal. In general the Wigner-Gabor signal is not the analytic signal, although for a pure tone A cos(ω 0 t) the Wigner-Gabor and analytic signals both equal A exp(jω 0 t). Also, for a time-limited signal s(t) = 0, |t| > T, the analytic signal is not time-limited, but the Wigner-Gabor signal is time-limited.