Envisioned speech recognition using EEG sensors

Recent advances in EEG technology makes brain-computer-interface (BCI) an exciting field of research. BCI is primarily used to adopt with the paralyzed human body parts. However, BCI in envisioned speech recognition using electroencephalogram (EEG) signals has not been studied in details. Therefore, developing robust speech recognition system using EEG signals was proposed. In this paper, we propose a coarse-to-fine-level envisioned speech recognition framework with the help of EEG signals that can be thought of as a serious contribution in this field of research. Coarse-level classification is used to differentiate/categorize text and non-text classes using random forest (RF) classifier. Next, a finer-level imagined speech recognition of each class has been carried out. EEG data of 30 text and not-text classes including characters, digits, and object images have been imagined by 23 participants in this study. A recognition accuracy of 85.20 and 67.03% has been recorded at coarse- and fine-level classifications, respectively. The proposed framework outperforms the existing research work in terms of accuracy. We also show the robustness in envisioned speech recognition.

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