Brain Waves Pattern Recognition Using LSTM-RNN for Internet of Brain-Controlled Things (IoBCT) Applications

A great number of individuals suffer from spinal cord injuries which lead to drastic limitations to their daily life functions. Specifically, suffering from paraplegia and quadriplegia will deem the patient unable to move two limbs or all four limbs respectively. However, even if the spine is damaged, this does not stop the brain from functioning, and it will still have the ability to distinguish objects. Further, the implementation of Brain- Machine Interface (BMI)-based IoT system suffers from several challenges such as the issues of accurately translating the user intention. This paper presents a method for brain waves recognition using deep learning (DL) based on shapes and colors for use in merging concepts of the internet of things (IoT) and the brain computer interface (BCI) which we wish to call "Internet of Brain Controlled Things or (IoBCT) in short. The results showed an acceptable accuracy of 0.93 in the brain waves pattern’s recognition which opens the way towards designing a reliable IoBCT.