A brain computer interface system for the improvement of cognitive and communication abilities for patients with neuromuscular disorders

Abstract

More than 16 million people in the United States are living with cognitive impairment. Reports suggest that 12,000-15,000 people have amyotrophic lateral sclerosis and approximately 17 million people have cerebral palsy, globally. The engineering goals are (i) to design an experimental study to analyze and improve cognitive performance and (ii) to develop a communication aid to support people with neuromuscular disorders using non-invasive brain-computer interface. A low-cost EEG device, Emotiv EPOC+ is used to record EEG data and a Python interface is used to stream the data for analysis. The features extracted are used to train the classifier, Linear Discriminant Analysis. The participants' cognitive performances were measured initially and after giving 20 days of feedback sessions with alpha-numeric speller. The performances were in the range of 76%- 81% initially and accuracy improved for all the participants and are in the range of 84%-89.3% after feedback sessions. The proposed feedback training design is an excellent way to improve cognitive abilities and can be used for healthy individuals as well as people with attention deficiency to improve their attention. A software application is developed to use the system as a communication aid for neuromuscular disorder patients who are unable to communicate. The accuracy in identifying the words of participants' choice are measured only using their brain activity. The communication aid was able to predict more than 91% of the words correctly. The system is low-cost and easy-to-use with a short setup time regardless of users' expertise.