IMPLEMENTING MACHINE LEARNING PARADIGMS FOR DECODING OF INNER SPEECH COMMANDS: AN EEG-BCI STUDY
Sahar Jahanikia, Deniz Yilmaz, Ritwik Jayaraman, Jeonghyun An, Manushri Dhanakoti, Krrish Ganesh, Sarah Le, Sushmita Musunuri, Shravani Vedagiri · IBRO Neuroscience Reports · 2023
The ability to detect inner speech, a type of speech formulated solely in thought, from electroencephalography (EEG) signals has been shown to have applications in assisting individuals with communication impairments. By developing a Brain-Computer Interface (BCI) pipeline using machine learning to classify EEG patterns associated with inner speech, we can create external software that translates brain signals into actions. The resulting BCI could significantly improve the quality of life of individuals with communication impairments.