||An IoT based Cloud EEG Signal Analytic Framework for Thought to Text Mapping
||(A. Joshua Jafferson) ; (Vijayakumar Ponnusamy) ; (Jovana Jovic) ; (Miroslav Trajanovic)
|| AWS lambda; Brain-computer interface; Cloud computing; CNN; EEG signal; IoT; Imagined speech to text
||Paralyzed people have difficulty communicating with the world for their daily basic needs, and their caretakers have difficulty understanding their needs. The development and implementation of a handheld device-based brain-computer interface system with machine learning will solve the above problem. On the other hand, a simple handheld device cannot satisfy the computation of hunger ML algorithms and will have more latency. This paper overcomes the limitations of the above by processing the data in the cloud. The handheld device reads and preprocesses the electroencephalogram (EEG) data and forwards it to the IoT-based Cloud server. The cloud server applies the machine-learning algorithm and classifies it in the text, representing the word thought by the user. This text information result is sent back to the handheld device and intimates the caretaker to know the patient's needs. The evaluation result of the proposed system for ten words to deal with the basic needs highlights the feasibility of implementing it in practice.