Word-Based Classification of Imagined Speech Using EEG

Abstract

Imagined speech is a process where a person imagines the sound of words without moving any of his or her muscles to actually say the word. If the brain signals of a person imagining the speech can be used to recognize the actual words intended to be spoken, this could be a huge step towards helping people with physical disabilities such as locked-in syndrome to have effective communication with others. This can also prove to be useful in situation where visual or audible communication is undesirable, for instance in military situation. Recent advancement in technologies and devices for capturing brain signals, particularly electroencephalogram (EEG), has made the research in recognizing imagined speech possible. While these are still in early years, published studies have shown promising results in this particular area of research. Current approaches in recognizing imagined speech can generally be divided into two, syllable-based and word-based. In this paper, we proposed a simple word-based approach using Mel Frequency Cepstral Coefficients (MFCC) and k-Nearest Neighbor (k-NN) towards recognizing two simple words using EEG signals. Despite its simplicity, the results obtained show some improvements to other studies based on dry EEG electrode device.