Classification based on sparse representations of attributes derived from empirical mode decomposition in a multiclass problem of motor imagery in EEG signals

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.