EEG based word familiarity using features and frequency bands combination

Abstract

The appearance of unknown words often disturbs communication and reading. This paper focuses on detecting those words which are unfamiliar to the users while reading using Electroencephalography (EEG). In particular, a word-familiarity prediction approach based on EEG signals from the user's brain waves has been developed. Word-familiarity refers whether a user is familiar with the word or not while reading the text. The recorded signals have been processed using Wavelet decomposition technique and four features, namely mean, standard deviation, Root Mean Square (RMS) and power have been computed from beta and gamma frequency bands. The prediction of word-familiarity has been performed using Random Forest (RF) classifier. Next, a decision fusion approach has also been used to boost the prediction performance. A dictionary based pop-up window has been developed to provide the meaning of the word when a user is found to be unfamiliar with the text. EEG dataset of 12 users has been developed while they are reading 25 words. The results show that the characteristics of brain waves at the time of unknown word perception can be detected, i.e., when the user comes across an unknown word then the pattern of his brain waves comes out to be totally different from the pattern when he is familiar with the word. Similarly, the pattern of brain waves has been found to be different for the word for which the user has confusion in his mind. An accuracy of 82% has been recorded using the proposed classifier combination approach. Finally, a comparative study with other popular classification technique is also discussed. © 2017 Elsevier B.V.