Open Access

ARTICLE

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Umashankar Ganesan^1,*, A. Vimala Juliet², R. Amala Jenith Joshi³

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

* Corresponding Author: Umashankar Ganesan. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 2849-2863. https://doi.org/10.32604/iasc.2023.033759

Received 27 June 2022; Accepted 29 September 2022; Issue published 15 March 2023

Abstract

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, electroencephalogram (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). Subsequently, 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 movements in the parietal region are compared with the frequency changes that occurred in frontal EEG signals. This proposed algorithm also uses the deep learning 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 learning 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 specialists in their decision-making by facilitating the analysis and interpretation of brain signals in the field of neuroscience, specifically in tremor analysis in PD.

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