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The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. sparse representation classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical mode decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of features. In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use multilayer perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Feature selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. SRC and SRMLP achieve an average accuracy of 75.95 % and 82.51 % respectively while the MLP is 72.38 % , representing a gain between 4.93 % and 14 % . The use of EMD in relation to other feature processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP, etc.) have on average a performance close to other conventional models, but without surpassing them. The best sparse models achieve an average accuracy of 95.43 % among the subjects in the base, while other model reach 98.33 % . The improvement of self-adaptive mechanisms that respond efficiently to the user’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.

Purpose The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. Sparse Representation Classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical Mode Decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of attributes. Methods In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use Multilayer Perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Attribute selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Results Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. The SRC achieves an average accuracy of 83.07% while the MLP is 71.71%, representing a gain of over 15.84%. The use of EMD in relation to other attribute processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP etc.) do not achieve the performance of other conventional models. The best sparse models achieve an average accuracy of 66.7% among the subjects in the base, while other models reach 76.05%. Conclusion The improvement of self-adaptive mechanisms that respond efficiently to the user’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.

The aim of this study was to analyze the effects of production and economic viability of commercial diets with different protein sources associated with a 50% reduction of artemia in the feeding of Penaeus vannamei post-larvae. Four treatments were used in a completely randomized design, with four repetitions: T1—krill flour and salmon extract (78% CP + 50% reduction of artemia); T2—fish meal and mussel meal (70% CP + 50% reduction of artemia); T3—squid meal (63% CP + 50% reduction of artemia); and T4—canola meal (48% CP + 50% reduction of artemia). The commercial feeds were offered into 16 experimental units, for 18 days, from hatching to post-larvae harvest. Total number of larvae, survival, final weight, and biomass were analyzed by ANOVA followed by Tukey test. Greatest survival (51.47%) was achieved by the animals in T1. Biomass was higher in T1 and T2 (18.35 and 20.51 g, respectively) when compared to T3 and T4. We concluded that commercial feed consisting of quality marine protein and protein levels equal to or above 70% provide better zootechnical indexes and financial gains in P. vannamei hatchery, even with a 50% reduction in the supply of artemia.