Neuro-Phone: An assistive framework to operate smartphone using EEG signals

Abstract

Recent advances in the brain computing technology have opened up novel paths for the development of various Brain-Computer-Interface (BCI) applications. It enables people to use the signals of BCI for silent communications, biometrics, security and to control various devices. Likewise, the progress in Smartphone technology facilitates everyone with a large number of applications including gaming, computing, banking, etc. Hence, the usage of mobile phones is increasing day by day. However, there are persons who suffer from various physical illnesses and unable to operate smartphones. In this paper, we propose an assistive framework 'Neuro-phone' to operate Smartphones using Electroencephalographic (EEG) signals by person with disability. The framework is able to perform basic operations of mobile phone as per the brain wave instructions. The analysis of the signals have been performed using Discrete Fourier Transform (DFT) and the classification has been performed using Hidden Markov Model (HMM) classifier. EEG signals of 9 mental commands from 8 participants have been recorded using an Android operated Smartphone. An accuracy of 68.69% has been recorded using HMM based classification. The results show the efficacy of the proposed framework that can be used in future mobile-BCI applications and other healthcare assistive techniques. � 2017 IEEE.