Analysis of the moderating effect of music therapy on autism in the context of big data

Abstract

In this paper, in the process of analyzing the response of music therapeutic method to emotion regulation, eye movement data, cognitive data, and EEG signal data were collected to clarify the criteria for the selection of indicators. The wavelet transform algorithm is used to decompose the original EEG signal, extract the features of the EEG signal based on the power spectrum, and improve its coherence. The behavioral intervention for autistic children was specifically addressed through the use of Orff music therapy following the pre-investigation of the subjects. The moderating effect of the music therapy method on the children’s four-eye-relative behavior was analyzed in conjunction with the number of occurrences of the children’s target behaviors in the baseline, intervention, and tracking periods. To explore the cumulative acquisition number and generalization maintenance effects of children’s responses to medium- versus fast-speed music at different teaching sessions in conjunction with the convergence and stability values of response behaviors within the phases. The combination of the music therapy method and the Go/No-go task method was used to analyze the effect of the number of music training sessions on response inhibition in autism. The data showed that the mean of correctness of responses for the second medium-speed music instruction for subject W was 0.495. The highest mean for the three fast music instruction was 0.801. All three subjects maintained a high level of correctness during the maintenance period after the intervention was withdrawn. It has been suggested that music training has a significant impact on improving inhibitory control in children with autism.

Keywords

• wavelet transform algorithm
• eeg signals
• behavioral intervention
• music therapeutic method
• eye movement data
• 00a73