  Technology advancements made it easy to measure non-invasive and high-quality
electroencephalograph (EEG) signals from human's brain. Hence, development of
robust and high-performance AI algorithms becomes crucial to properly process
the EEG signals and recognize the patterns, which lead to an appropriate
control signal. Despite the advancements in processing the motor imagery EEG
signals, the healthcare applications, such as emotion detection, are still in
the early stages of AI design. In this paper, we propose a modular framework
for the recognition of vowels as the AI part of a brain computer interface
system. We carefully designed the modules to discriminate the English vowels
given the raw EEG signals, and meanwhile avoid the typical issued with the
data-poor environments like most of the healthcare applications. The proposed
framework consists of appropriate signal segmentation, filtering, extraction of
spectral features, reducing the dimensions by means of principle component
analysis, and finally a multi-class classification by decision-tree-based
support vector machine (DT-SVM). The performance of our framework was evaluated
by a combination of test-set and resubstitution (also known as apparent) error
rates. We provide the algorithms of the proposed framework to make it easy for
future researchers and developers who want to follow the same workflow.
