Search

Abstract: A new method for investigating mental fatigue based on P300 variability is presented here. In this approach a new coupled particle filtering for tracking variability of P300 subcomponents, i.e., P3a and P3b, across trials is developed. The latency, amplitude, and width of each subcomponent, as the main varying parameters, are modelled using state space system. In this model the observation is modelled as a linear function of amplitude and a nonlinear function of latency and width. Two Rao-blackwellised particle filters are then coupled and employed for recursive estimation of the state of the system across trials. By including some physiological based constraints, the proposed technique prevents generation of invalid particles during estimation of the state of the system. The main advantage of the algorithm compared with other single trial based methods is its robustness in the low signal-to-noise ratio situations. The method is applied to both simulated data and real mental fatigue data. The results demonstrate potential use of the method in event-related potential (ERP) based applications. [Copyright &y& Elsevier]

Abstract: Localization of seizure sources prior to neurosurgery is crucial. In this paper, a new method is proposed to localize the seizure sources from multi-channel electroencephalogram (EEG) signals. Blind source separation based on second order blind identification (SOBI) is primarily applied to estimate the brain source signals in each window of the EEG signals. A new clustering method based on rival penalized competitive learning (RPCL) is then developed to cluster the rows of the estimated unmixing matrices in all the windows. The algorithm also includes pre and post-processing stages. By multiplying each cluster center to the EEG signals, the brain signal sources are approximated. According to a complexity value measure, the main seizure source signal is separated from the others. This signal is projected back to the electrodes’ space and is subjected to the dipole source localization using a single dipole model. The simulation results verify the accuracy of the system. In addition, correct localization of the seizure source is consistent with the clinical tests derived using the simultaneous intracranial recordings. [Copyright &y& Elsevier]

This paper addresses the application of digital signal processing (DSP) techniques to the robust measurement of acoustical features of the human voice. It then addresses the use of regression based techniques for the estimation of grade, roughness, breathiness, asthenia and strain, from these acoustical features. These five properties of voice are the basis of the widely used 'GRBAS' characterisation of voice disorders. A well-known cross-correlation technique has been enhanced for more reliably measuring the fundamental frequency of vowels which is crucial for the derivation of acoustic features such as the harmonic-to-noise-ratio, jitter and shimmer. Regression techniques including K-Nearest Neighbour Regression and Multiple Linear Regression are employed for derivation of GRBAS properties. Validation of the enhanced cross-correlation technique against well established published or commercially available techniques has been carried out by analysing synthetic sustained vowels. It was found that the enhanced method is capable of producing more reliable and robust measurements, in the context of our experiments, than the well-established Praat technique and Multi-Dimensional-Voice-Program (MDVP) software, especially in cases where the signal to noise ratio is low. Estimation of GRBAS components using our methods has been found to be in good agreement with traditional GRBAS scoring by speech and language therapists (SLTs). Voice analysis using DSP to extract acoustic features has the potential for objective and computerised GRBAS voice assessment. Such assessment can usefully augment GRBAS assessment as traditionally carried out subjectively by SLTs. [Display omitted] [ABSTRACT FROM AUTHOR]