Wavelet-based imagined speech classification using electroencephalography Online publication date: Thu, 07-Apr-2022
by Dipti Pawar; Sudhir Dhage
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 38, No. 3, 2022
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.
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