Article ; Online: Motor imagery classification using sparse representations
Scientific Reports, Vol 13, Iss 1, Pp 1-
an exploratory study
2023 Volume 24
Abstract: Abstract 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 ...
| Abstract | Abstract 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 $$75.95\%$$ 75.95 % and $$82.51\%$$ 82.51 % respectively while the MLP is $$72.38\%$$ 72.38 % , representing a gain between $$4.93\%$$ 4.93 % and $$14\%$$ 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 $$95.43\%$$ 95.43 % among the subjects in the base, while other model reach $$98.33\%$$ 98.33 % . The ... |
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| Keywords | Medicine ; R ; Science ; Q |
| Subject code | 004 |
| Language | English |
| Publishing date | 2023-09-01T00:00:00Z |
| Publisher | Nature Portfolio |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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