A Novel BCI - based Silent Speech Recognition using Hybrid Feature Extraction Techniques and Integrated Stacking Classifier

The Brain Computing Interface (BCI) is a technology that has resulted in the advancement of Neuro-Prosthetics applications. BCI establishes a connection between the brain and a computer system, primarily focusing on assisting, enhancing, or restoring human cognitive and sensory - motor functions. BCI technology enables the acquisition of Electroencephalography (EEG) signals from the human brain. This research concentrates on analyzing the articulatory aspects, including Wernicke's and Broca's areas, for Silent Speech Recognition. Silent Speech Interfaces (SSI) offers an alternative to conventional speech interfaces that rely on acoustic signals. Silent Speech refers to the process of communicating speech in the absence of audible and intelligible acoustic signals. The primary objective of this study is to propose a classifier model for phoneme classification. The input signal undergoes preprocessing, and feature extraction is carried out using traditional methods such as Mel Frequency Cepstrum Coefficients (MFCC), Mel Frequency Spectral Coefficients (MFSC), and Linear Predictive Coding (LPC). The selection of the best features is based on classification accuracy for a subject and is implemented using the Integrated Stacking Classifier. The Integrated Stacking Classifier outperforms other traditional classifiers, achieving an average accuracy of 75% for both thinking and speaking states on the KaraOne dataset and approximately 86.2% and 84.09% for thinking and speaking states on the Fourteen Channel EEG for Imagined Speech (FEIS) dataset.