eprintid: 14117 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/41/17 datestamp: 2023-11-10 03:28:41 lastmod: 2023-11-10 03:28:41 status_changed: 2023-11-10 01:56:03 type: article metadata_visibility: show creators_name: Al-Quraishi, M.S. creators_name: Elamvazuthi, I. creators_name: Tang, T.B. creators_name: Al-Qurishi, M. creators_name: Parasuraman, S. creators_name: Borboni, A. title: Multimodal Fusion Approach Based on EEG and EMG Signals for Lower Limb Movement Recognition ispublished: pub keywords: Biomedical signal processing; Electroencephalography; Electrophysiology; Joints (anatomy); Modal analysis; Motion estimation, Ankle joints; Electromyography signals; Foot; Joint movement; Limb movements; Lower limb; Multi-modal; Multi-modal fusion; Muscular fatigues; Pattern recognition ankle joint movement, Electromyography note: cited By 13 abstract: In this study, the fusion of cortical and muscular activities based on discriminant correlation analysis DCA) is developed to recognize bilateral lower limb movements. Electromyography (EMG) and electroencephalography (EEG) signals were concurrently recorded from 28 healthy subjects while performing various ankle joint movements. The two types of biosignals were fused at feature level, and five different classifiers were used for the purpose of movement recognition. The performance of the classifiers with multimodal and single modality data were assessed with five different sampling window sizes. The results demonstrated that the use of a multimodal approach results in an improvement of the classification accuracy with a linear discriminator analysis classifier (LDA). The highest recognition accuracy was 96.64 ± 4.48 with a window size of 250 sample points, in contrast with 89.99 ± 7.94 for EEG data alone. Furthermore, the multimodal fusion based on DCA was validated with fatigued EMG signal to investigate the robustness of the fusion technique against the muscular fatigue. In addition, the statistical analysis result demonstrates that the proposed fusion approach provides a substantial improvement in motion recognition accuracy 96.64 ± 4.48 (p < 0.0001) compared to method based on a single modality. © 2001-2012 IEEE. date: 2021 publisher: Institute of Electrical and Electronics Engineers Inc. official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117259297&doi=10.1109%2fJSEN.2021.3119074&partnerID=40&md5=aee507ca5c2fb5336c6711fe63130308 id_number: 10.1109/JSEN.2021.3119074 full_text_status: none publication: IEEE Sensors Journal volume: 21 number: 24 pagerange: 27640-27650 refereed: TRUE issn: 1530437X citation: Al-Quraishi, M.S. and Elamvazuthi, I. and Tang, T.B. and Al-Qurishi, M. and Parasuraman, S. and Borboni, A. (2021) Multimodal Fusion Approach Based on EEG and EMG Signals for Lower Limb Movement Recognition. IEEE Sensors Journal, 21 (24). pp. 27640-27650. ISSN 1530437X