Abstract
Brain-Computer Interface (BCI) system allows physically challenged people to operate with their external surroundings, just through their brain signals. Since the objective of BCI is to categorize the brain signals into homogeneous classes each of which represents a mental state, it is necessary to choose an appropriate discrimination approach. So, we use the Support Vector Machines (SVM) due to their multiple benefits. The SVM are suggested to treat binary problems, their conversion to multiclass cases (M-SVM) includes: indirect methods based on decomposition approaches, and direct methods that consider all classes simultaneously. This experiment aims to introduce the use of the four existing direct M-SVM in the problematic of mental states recognition. The discriminators operate independently and give probability estimates relative to five mental states. Results indicate that models generate nearly similar accuracies. Nevertheless, with an average rates ranging from 68.25% to 90.86%, Crammer and Singer discriminator outperforms the other models.
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Hendel, M., Hendel, F. (2019). Using Probabilistic Direct Multi-class Support Vector Machines to Improve Mental States Based-Brain Computer Interface. In: Rocha, Á., Serrhini, M. (eds) Information Systems and Technologies to Support Learning. EMENA-ISTL 2018. Smart Innovation, Systems and Technologies, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-030-03577-8_35
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DOI: https://doi.org/10.1007/978-3-030-03577-8_35
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