國立虎尾科技大學 |

Human Machine Interfaces Using Multichannel Physiological Signals.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Human Machine Interfaces Using Multichannel Physiological Signals./
作者:	Patil, Reena.
面頁冊數:	1 online resource (67 pages)
附註:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355593891

Human Machine Interfaces Using Multichannel Physiological Signals.
Patil, Reena.

Human Machine Interfaces Using Multichannel Physiological Signals. - 1 online resource (67 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--San Diego State University, 2017.

Includes bibliographical references

The field of medicine and technology are converging together to realize the dream of a healthier future through effective utilization of scientific advances. One of such remarkable development in global healthcare is brain-computer interface (BCI), which aims at decoding user's intentions and motions directly from brain signals. For building such BCI, first it is important to study the association between brain and muscle signals, then the association between muscle signals and kinematic motion to identify/recreate specific motions. In this study, neural and muscular signal associations which are needed to develop BCI are explored. The part-A of the thesis presents the relation between brain and muscle signal during limb kinematics/motion. The part-B of the thesis presents a comprehensive study of the ability of the muscle signals to identify different limb motions. These studies together will be instrumental in creating a BCI system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355593891Subjects--Topical Terms:

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

554714
Electronic books.

Human Machine Interfaces Using Multichannel Physiological Signals.
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500 $a Adviser: Yusuf Ozturk.
502 $a Thesis (M.S.)--San Diego State University, 2017.
504 $a Includes bibliographical references
520 $a The field of medicine and technology are converging together to realize the dream of a healthier future through effective utilization of scientific advances. One of such remarkable development in global healthcare is brain-computer interface (BCI), which aims at decoding user's intentions and motions directly from brain signals. For building such BCI, first it is important to study the association between brain and muscle signals, then the association between muscle signals and kinematic motion to identify/recreate specific motions. In this study, neural and muscular signal associations which are needed to develop BCI are explored. The part-A of the thesis presents the relation between brain and muscle signal during limb kinematics/motion. The part-B of the thesis presents a comprehensive study of the ability of the muscle signals to identify different limb motions. These studies together will be instrumental in creating a BCI system.
520 $a Part-A of the study focuses on understanding characteristics of electrocorticography (ECoG) and electromyography (EMG) signals. Their relation to naturalistic reaching and grasping movement are also explained in the same study. Magnitude square coherence between neural and muscle signals recorded from different electrodes are analyzed in the frequency domain. This study detects and presents the best ECoG electrodes and frequency bands which can be used for decoding of EMG signal. Part-B detects and classifies motion pattern from surface electromyography (SEMG). This study proposes a multichannel spectral model for detection and classification of upper limb motion using autoregressive (AR) model coefficients. The method proposed in this thesis, successfully classified 7 different upper limb motions with an accuracy of 92.71%.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a San Diego State University. $b Electrical Engineering. $3 1182874
773 0 $t Masters Abstracts International $g 57-02(E).
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