國立虎尾科技大學 |

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation./
作者:	Tryon, Jacob G.
面頁冊數:	1 online resource (424 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798381029086

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.
Tryon, Jacob G.

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation. - 1 online resource (424 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.

Includes bibliographical references

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG-EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG-EMG fusion and to develop a novel control system based on the incorporation of EEG-EMG fusion classifiers.A dataset of EEG and EMG signals were collected during dynamic elbow flexion-extension motions and used to develop EEG-EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decisionlevel fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG-EMG fusion can classify more indirect tasks.A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG-EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG-EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381029086Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.
LDR:03869ntm a22003497 4500 001 1147692
005 20240916065441.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381029086
035 $a (MiAaPQ)AAI30737631
035 $a (MiAaPQ)oaiirlibuwocaetd12347
035 $a AAI30737631
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Tryon, Jacob G. $3 1473469
245 1 0 $a Evaluating EEG-EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation.
264 0 $c 2023
300 $a 1 online resource (424 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Trejos, Ana Luisa.
502 $a Thesis (Ph.D.)--The University of Western Ontario (Canada), 2023.
504 $a Includes bibliographical references
520 $a Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG-EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG-EMG fusion and to develop a novel control system based on the incorporation of EEG-EMG fusion classifiers.A dataset of EEG and EMG signals were collected during dynamic elbow flexion-extension motions and used to develop EEG-EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decisionlevel fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG-EMG fusion can classify more indirect tasks.A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG-EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG-EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Robotics. $3 561941
650 4 $a Signal processing. $3 561459
650 4 $a Neural networks. $3 1011215
650 4 $a Electrodes. $3 581312
650 4 $a Co authorship. $3 1372838
650 4 $a Load. $3 1437809
655 7 $a Electronic books. $2 local $3 554714
690 $a 0771
690 $a 0800
690 $a 0544
710 2 $a The University of Western Ontario (Canada). $3 1184598
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-06B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30737631 $z click for full text (PQDT)