Spectral Analysis and Validation of Parietal Signals for Different Arm Movements  

作　　者：Umashankar Ganesan A.Vimala Juliet R.Amala Jenith Joshi 

机构地区：[1]School of Electrical and Electronics Engineering,Sathyabama Institute of Science and Technology,Chennai,600119,India [2]School of Electronics and Instrumentation Engineering,SRM Institute of Science and Technology,Chennai,630203,India [3]Department of Biomedical and Mechanical Engineering,School of Engineering,National University of Ireland,Galway,H91 CF50,Ireland

出　　处：《Intelligent Automation & Soft Computing》2023年第6期2849-2863,共15页智能自动化与软计算（英文）

摘　　要：Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the speci

关 键 词：Parietal EEG signals fast fourier transform Levenberg-Marquardt algorithm haar wavelet daubechies wavelet statistical analysis 

分 类 号：R74[医药卫生—神经病学与精神病学]