An algorithm for onset detection of linguistic segments in continuous electroencephalogram signals
This work is significant for developing fully asynchronous Brain Computer Interfaces based on imagined words, which could benefit individuals seeking to control external devices through thought.
This paper addresses the problem of detecting the onset of imagined words in continuous electroencephalogram (EEG) signals for Brain Computer Interfaces (BCI). The authors achieved a True Positive Rate of 0.69 and 0.77 for onset detection with a timing error tolerance of 3 and 4 seconds, respectively, using features based on the generalized Hurst exponent.
A Brain Computer Interface based on imagined words can decode the word a subject is thinking on through brain signals to control an external device. In order to build a fully asynchronous Brain Computer Interface based on imagined words in electroencephalogram signals as source, we need to solve the problem of detecting the onset of the imagined words. Although there has been some research in this field, the problem has not been fully solved. In this paper we present an approach to solve this problem by using values from statistics, information theory and chaos theory as features to correctly identify the onset of imagined words in a continuous signal. On detecting the onsets of imagined words, the highest True Positive Rate achieved by our approach was obtained using features based on the generalized Hurst exponent, this True Positive Rate was 0.69 and 0.77 with a timing error tolerance region of 3 and 4 seconds respectively.