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Technology advancements made it easy to measure non-invasive and high-qualityelectroencephalograph (EEG) signals from human's brain. Hence, development ofrobust and high-performance AI algorithms becomes crucial to properly processthe EEG signals and recognize the patterns, which lead to an appropriatecontrol signal. Despite the advancements in processing the motor imagery EEGsignals, the healthcare applications, such as emotion detection, are still inthe early stages of AI design. In this paper, we propose a modular frameworkfor the recognition of vowels as the AI part of a brain computer interfacesystem. We carefully designed the modules to discriminate the English vowelsgiven the raw EEG signals, and meanwhile avoid the typical issued with thedata-poor environments like most of the healthcare applications. The proposedframework consists of appropriate signal segmentation, filtering, extraction ofspectral features, reducing the dimensions by means of principle componentanalysis, and finally a multi-class classification by decision-tree-basedsupport vector machine (DT-SVM). The performance of our framework was evaluatedby a combination of test-set and resubstitution (also known as apparent) errorrates. We provide the algorithms of the proposed framework to make it easy forfuture researchers and developers who want to follow the same workflow.

Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design