An EEG signal-based music treatment system for autistic children using edge computing devices

This paper proposes a system that applies electroencephalogram (EEG) technology to achieve music intervention therapy. The system can identify emotions of autistic children in real-time and play music considering their emotions as a musical treatment to assist the treatment of music therapists and the principle of playing homogenous music is to finally calm people down. The proposed method firstly collects EEG of autistic children using a 14-channel EMOTIV EPOC + and preprocesses signals through bandpass filtering, wavelet decomposition and reconstruction, then extracts frequency band-power characteristics of reconstructed EEG signals. Later, the data are classified as one of the three types of emotions (positive, middle and negative) using a support vector machine (SVM). The system also displays the recognized emotion type on a user interface and gives real-time emotional state feedback on emotional changes, which helps music therapists to evaluate the treatment and results more conveniently and effectively. Real EEG data are used to conduct the verification of system feasibility which reaches a classification accuracy of 88%. As the Internet of Things develops, the combination of edge computing with Wise Information Technology of 120 (WIT120) becomes a new trend. In this work, we propose a system to combine edge computing devices with cloud computing resources to form the music regulation system for autistic children to meet processing requirements for EEG signals in terms of timeliness and computational performance. In the designed system, preprocessing EEG signals is done in edge nodes then the preprocessed signals are sent to the cloud where frequency band-power characteristics can be extracted as features to be used in SVM. At last, the results are sent to a mobile app or computer software for therapists to evaluate.