Examining Temporal Variations in Recognizing Unspoken Words using EEG Signals

Studies on recognising unspoken speech with the use of electroencephalographic (EEG) signals vary in their designs. The participants are either asked to imagine unspoken speech within a specific time frame, or alternatively indicate the start and end of the imagined speech. Optimizing the length and training size of imagined speech is important to improve the rate and speed of recognizing unspoken speech in on-line applications. In this study, we recorded EEG data when the participants performed unspoken speech of five words using two technologies: (1) marking the start and end of the trial by using mouse clicks and (2) performing the imagination in a four-second fixed time window. Four classifiers were trained in all experiment parts: support vector machine, naive bayes, random forest, and linear discriminate analysis. The results show that the best time frame is 3.5-4 seconds length. Moreover, the increase in training size improve the average classification accuracy. However, this improvement becomes slight between 125-175 total training trials. The training data can be recorded in parts, however, the required training size should be increased to have better classification accuracy. In all analysis parts, random forest classifier shows better results among the other classifiers.