The influence of subliminal priming (behavior outside of awareness) in humans is an interesting phenomenon and its understanding is crucial as it can impact behavior, choices, and actions. Given this, research about the impact of priming continues to be an area of investigative interest, and this paper provides a technical overview of research design strengths and issues in subliminal priming research. Efficient experiments and protocols, as well as associated electroencephalographic and eye movement data analyses, are discussed in detail. We highlight the strengths and weaknesses of different priming experiments that have measured affective (emotional) and cognitive responses. Finally, very recent approaches and findings are described to summarize and emphasize state-of-the-art methods and potential future directions in research marketing and other commercial applications.

Skeletal muscle force can be estimated using surface electromyographic (sEMG) . Usually, the surface location for the sensors is near the respective muscle motor unit points. Skeletal muscles generate a spatial EMG signal, which causes cross talk between different sEMG signal sensors. In this study, an array of three sEMG sensors is used to capture the information of muscle dynamics in terms of sEMG signals. The recorded sEMG signals are filtered utilizing optimized nonlinear Half-Gaussian Bayesian filters parameters, and the muscle force signal using a Chebyshev type-II filter. The filter optimization is accomplished using Genetic Algorithms. Three discrete time state-space muscle fatigue models are obtained using system identification and modal transformation for three sets of sensors for single motor unit. The outputs of these three muscle fatigue models are fused with a probabilistic Kullback Information Criterion (KIC) for model selection. The final fused output is estimated with an adaptive probability of KIC, which provides improved force estimates.