Title: Wavelet-based imagined speech classification using electroencephalography

Authors: Dipti Pawar; Sudhir Dhage

Addresses: Department of Computer Engineering, Sardar Patel Institute of Technology, Andheri(W), Mumbai, India ' Department of Computer Engineering, Sardar Patel Institute of Technology, Andheri(W), Mumbai, India

Abstract: Oral communication is the natural way in which humans interact. However, in some circumstances, it is difficult to emit an intelligible acoustic signal, or it is desired to communicate without making sounds. In these conditions, systems that enable spoken communication in the absence of an acoustic signal is desirable. In this context, brain-computer interface (BCI) is a remarkable way of solving daily life problems. The major objective of the proposed research is to develop an imagined speech classification system based on electroencephalography (EEG); which consists of pre-processing, feature extraction and classification. In the pre-processing stage, EOG artefacts are removed via independent component analysis (ICA). Discrete wavelet transform (DWT) is used to extract wavelet-based features from EEG segments. Finally, the support vector machine (SVM) is employed for the discriminant of extracted features. This research achieves promising ends in classification accuracy compared with some of the most common classification techniques in BCI.

Keywords: electroencephalography; EEG; brain-computer interface; BCI; discrete wavelet transform; DWT; imagined speech; support vector machine; SVM.

DOI: 10.1504/IJBET.2022.121737

International Journal of Biomedical Engineering and Technology, 2022 Vol.38 No.3, pp.215 - 224

Received: 18 Oct 2018
Accepted: 16 Jan 2019
Published online: 07 Apr 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article