Classifying Imaginary Vowels from Frontal Lobe EEG via Deep Learning

Brain-Computer Interface (BCI) is a promising technology for individuals who suffer from motor or speech disabilities due to the process of decoding brain signals. This paper uses a dataset for imagined speech to classify vowels based on the neurological areas of the brain. The normalized cross-correlation matrices between two electrodes are used as features. We demonstrate that by using the EEG from the frontal region of the brain, we obtain higher than 85 percent accuracy for correct vowel decoding by using two types of neural networks: convolutional neural network (CNN) and long short-term memory (LSTM). This accuracy is higher than previous studies that have classified the dataset using the entire brain region. This work shows great promise for task decoding where the physiological regions of the brain associated with specific tasks are exploited. The proposed approach has the potential to be deployed in future BCI applications.

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