國立虎尾科技大學 |

Deep learning for rapid serial visual presentation event from electroencephalography signal.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Deep learning for rapid serial visual presentation event from electroencephalography signal./
作者:	Mao, Zijing.
面頁冊數:	1 online resource (178 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369440386

Deep learning for rapid serial visual presentation event from electroencephalography signal.
Mao, Zijing.

Deep learning for rapid serial visual presentation event from electroencephalography signal. - 1 online resource (178 pages)

Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2016.

Includes bibliographical references

The goal of bio-inspired machine research is to create and apply a new-generation interaction between human and machine for our life. It allows machine to be directly triggered by users' simultaneous responses. Brain computer interface (BCI), which allows human subjects to communicate with or control an external device with their brain signals, belongs to one type of such research. There are many paradigms for BCI systems, among which one paradigm named rapid serial visual presentation (RSVP) tasks. RSVP aims at collecting personal EEG when tested subjects are asked to identify a target image from a continuous burst of image clips presented at a high rate. Subjects are required to search for target images from a large collection of undesirable ones, and the target image can be predefined or decided by certain rules. Our goal for RSVP tasks is to use machine learning, especially deep learning algorithms to predict whether subjects have seen a target or not.

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

Mode of access: World Wide Web

ISBN: 9781369440386Subjects--Topical Terms:

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

554714
Electronic books.

Deep learning for rapid serial visual presentation event from electroencephalography signal.
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245 1 0 $a Deep learning for rapid serial visual presentation event from electroencephalography signal.
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300 $a 1 online resource (178 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-06(E), Section: B.
500 $a Adviser: Yufei Huang.
502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2016.
504 $a Includes bibliographical references
520 $a The goal of bio-inspired machine research is to create and apply a new-generation interaction between human and machine for our life. It allows machine to be directly triggered by users' simultaneous responses. Brain computer interface (BCI), which allows human subjects to communicate with or control an external device with their brain signals, belongs to one type of such research. There are many paradigms for BCI systems, among which one paradigm named rapid serial visual presentation (RSVP) tasks. RSVP aims at collecting personal EEG when tested subjects are asked to identify a target image from a continuous burst of image clips presented at a high rate. Subjects are required to search for target images from a large collection of undesirable ones, and the target image can be predefined or decided by certain rules. Our goal for RSVP tasks is to use machine learning, especially deep learning algorithms to predict whether subjects have seen a target or not.
520 $a We performed a comprehensive investigation on deep learning algorithm based classification predicting the target vs. non-target EEG epochs. The testing scenarios includes both time-locked RSVP tasks and non-time locked RSVP tasks with 6 different EEG experiments. The investigation of deep learning algorithm includes deep stacking network (DSN), deep neural network (DNN) and deep convolutional network (CNN). We also proposed the feature visualization methods for DSN and investigated the deconvolutional network as the visualization technique for CNN. The deep learning transferability is also investigated by our proposed DSN transfer learning and CNN transfer learning model on RSVP data. In addition, calibration sample size for classification in BCI systems have also been investigated and new feature combinations that will provide robust improvement in RSVP classification accuracy have been tested. In sum, we have studied DL solutions to classify BCI, especially RSVP tasks and provide methods dealing with experiment and subject dynamics for BCI tasks.
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
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Texas at San Antonio. $b Electrical & Computer Engineering. $3 845615
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10246258 $z click for full text (PQDT)