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it has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. Continuous spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ecog) recordings. Techniques from automatic speech recognition (asr), thereby transforms brain activity while speaking into the corresponding textual or digital representation. Speech is produced in the human cerebral cortex. Brain waves associated with speech processes can be directly recorded with electrodes located on the surface of the cortex. Currently the system decodes the continuously spoken speech and transforms it into a textual/digital representation. Here the cortical information is combined with linguistic knowledge and machine learning algorithms to extract the most likely word sequences. The data that is extracted is currently based on audible speech processing. However, the results are an important first step for recognizing speech from thought alone. Thus, our paper mainly focuses on the primary steps as to how we could achieve this.

Application of Artificial Intelligence in Developing a Brain to Text System to Help the Disabled