A Novel Deep Capsule Neural Network for Vowel Imagery Patterns from EEG Signals

Speech imagery has recently been included in the design of Brain–Computer Interfaces to develop novel communication or control systems based on brain activity that does not need external stimulation like evoked potentials. Three types of speech imagery exist: imagining words, syllables, or vowels. Words are composed of syllables and syllables by consonants and vowels. However, imagining just vowels generates Speech-Related Potentials that reduce the complexity of the brain activity in Electroencephalographic signals. This paper proposes a new classifier method for communication or control purposes based on a novel Deep Capsule Neural Network for Vowel Imagery recognition. The method is named Capsules for Vowel Imagery (CapsVI). CapsVI has the appropriate number and size of convolution kernels to find the relevant features of the input. The size of the capsules is estimated based on the feature patterns found during the training. The class capsules were developed based on prototypes patterns of /a/, /u/, and/no vowel/ classes. The experiments were developed with the DaSalla dataset. Results indicate that capsules model the Speech-Related Potentials of Vowel Imagery correctly enough to generate the necessary information for vowel pairwise classification. Furthermore, the results also demonstrate that CapsVI recognizes vowels with an average accuracy of 93.32% on the pairwise classification, and the best precision by subject is 94.68%. These results are the best in Vowel Imagery recognition of the English language reported in the literature.