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This work presents a simple study of stochastic arithmetic complex number operators for addition and multiplication. Their usage is demonstrated by design of a sum of product circuit As the stochastic complex number operators need more control random streams than stochastic rational number operators, we optimized the number of random generators used in the real circuit. In the end our sum of product...
In this contribution we examine the use and utility of parallel HMM classification in single-trial movement-EEG classification of index finger reaching and grasping movement. Parallel HMMs allow us to easily utilize the information contained in multiple channels. Using HMM classifier output in parallel from examined EEG channels we have been able to achieve as good a classification score as with single...
This contribution examines the usage of low frequency components (< 5 Hz) in single trial EEG recordings obtained during right index finger movement for classification of reaching and grasping movements. These components contain delta band activity and Movement Related Potentials (MRPs) associated with the movements. Time-frequency development is used to classify the movements using Hidden Markov...
The contribution investigates the impact of frequency feature optimization on discriminating between movement-related EEG realisations associated with right shoulder elevation and right index finger flexion movements. Exhaustive search of subbands in the range from 5 to 45 Hz is performed. A classifier based on Hidden Markov Models is utilised. The results show a large variability of optimal settings...
The contribution presents a novel high-performace, low power BCI architecture allowing a single-chip implementation of a BCI device. FPGA platform is used to reach high performance and low power consumption; to speed up the development cycle, high-level synthesis of DSP algorithms is employed. A novel highly modular architecture with many advantages (configurability, possibility of independent development,...
This paper describes use of EEG signal as biometric characteristic for person identification. We focus on the problem of repeatability of the identification process, and influence of the movement-related EEG on results of identification. Used database of EEG signals consists of two sessions, obtained approximately one year apart. We use Frequency-Zooming Auto-Regression modeling and Mahalanobis distance-based...
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