Sonification and textification: Proposing methods for classifying unspoken words from EEG signals. (August 2017)

• Record Type: Journal Article
• Authors: González-Castañeda, Erick F.; Torres-García, Alejandro A.; Reyes-García, Carlos A.; Villaseñor-Pineda, Luis
• Is Part Of: Biomedical signal processing and control. Volume 37(2017)
• Page Start: 82
• Page End: 91
• Publication Date: 2017-08
• Subjects: Electroencephalogram -- Brain–Computer Interfaces -- Sonification -- Textification -- Imagined speech; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.10.012
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: EEG-based Brain–Computer Interfaces (BCI) are an alternative technique that aims to integrate people with severe motor disabilities to their environment. However, they still are not used in everyday life because BCIs are controlled by means of few intuitive electrophysiological sources. To address this problem, work has been carried out with the objective of classifying EEG signals recorded while imagining speech. In this paper, the emerging techniques of sonification and textification are applied to EEG signals, which allows us to characterize EEG signals as either an audio signal or a text document. The aim is to analyze whether these techniques can help to do a better discrimination or to highlight patterns, and assess which of the two methods performs better in order to improve classification results of unspoken words. For proving this we processed signals, from 27 subjects, with and without the application of the proposed methods of sonification and textification. The average accuracy rate using the original EEG signals, the EEG sonified signals and the EEG textified signals are 58.41 ± 12.41%, 63.82 ± 13.44% and 83.34 ± 6.12%, respectively. It can be clearly noticed that the methods of sonification and textification of EEG improve the classification rates obtained using the EEG signals in their original form.