國立虎尾科技大學 |

Empirical modeling of asynchronous scalp recorded and intracranial EEG potentials.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Empirical modeling of asynchronous scalp recorded and intracranial EEG potentials./
作者:	Kaur, Komalpreet.
面頁冊數:	1 online resource (109 pages)
附註:	Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
Contained By:	Dissertation Abstracts International76-01B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781321301953

Empirical modeling of asynchronous scalp recorded and intracranial EEG potentials.
Kaur, Komalpreet.

Empirical modeling of asynchronous scalp recorded and intracranial EEG potentials. - 1 online resource (109 pages)

Source: Dissertation Abstracts International, Volume: 76-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

A Brain-Computer Interface (BCI) is a system that allows people with severe neuromuscular disorders to communicate and control devices using their brain signals. BCIs based on scalp-recorded electroencephalography (s-EEG) have recently been demonstrated to provide a practical, long-term communication channel to severely disabled users. These BCIs use time-domain s-EEG features based on the P300 event-related potential to convey the user's intent. The performance of s-EEG-based BCIs has generally stagnated in recent years, and high day-to-day performance variability exists for some disabled users. Recently intracranial EEG (i-EEG), which is recorded from the cortical surface or the hippocampus, has been successfully used to control BCIs in experimental settings. Because these recordings are closer to the sources of the neural activity, i-EEG provides superior signal-to-noise ratio, spatial resolution, and broader bandwidth compared to s-EEG. However, because i-EEG requires surgery and the long-term efficacy for BCIs must still be explored, this approach is still not an option for patients. In order to improve s-EEG BCI performance, it is important understand the underlying brain phenomena and exploit the relationships between the s-EEG and generally superior i-BEG signals. Because the human head acts as a volume conductor consisting of the brain, cerebrospinal fluid, skull, and scalp tissue, linear mathematical models can be used to relate s-EEG and i-EEG. This dissertation presents unique s-EEG and i-EEG data that were recorded from the same subjects and used to develop novel empirical models to estimate s-EEG from i-EEG. These new empirical models can be used to better understand the sources and propagation of the relevant neural activity, as well as to validate existing theoretical volume conduction models. It is envisioned that this knowledge will help to advance algorithms for improving s-EEG BCI performance.

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

Mode of access: World Wide Web

ISBN: 9781321301953Subjects--Topical Terms:

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

554714
Electronic books.

Empirical modeling of asynchronous scalp recorded and intracranial EEG potentials.
LDR:03200ntm a2200337Ki 4500 001 912037
005 20180605073453.5
006 m o u
007 cr mn||||a|a||
008 190606s2014 xx obm 000 0 eng d
020 $a 9781321301953
035 $a (MiAaPQ)AAI3581599
035 $a AAI3581599
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Kaur, Komalpreet. $3 1184211
245 1 0 $a Empirical modeling of asynchronous scalp recorded and intracranial EEG potentials.
264 0 $c 2014
300 $a 1 online resource (109 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: Dissertation Abstracts International, Volume: 76-01(E), Section: B.
500 $a Adviser: Dean J. Krusienski.
502 $a Thesis (Ph.D.) $c Old Dominion University $d 2014.
504 $a Includes bibliographical references
520 $a A Brain-Computer Interface (BCI) is a system that allows people with severe neuromuscular disorders to communicate and control devices using their brain signals. BCIs based on scalp-recorded electroencephalography (s-EEG) have recently been demonstrated to provide a practical, long-term communication channel to severely disabled users. These BCIs use time-domain s-EEG features based on the P300 event-related potential to convey the user's intent. The performance of s-EEG-based BCIs has generally stagnated in recent years, and high day-to-day performance variability exists for some disabled users. Recently intracranial EEG (i-EEG), which is recorded from the cortical surface or the hippocampus, has been successfully used to control BCIs in experimental settings. Because these recordings are closer to the sources of the neural activity, i-EEG provides superior signal-to-noise ratio, spatial resolution, and broader bandwidth compared to s-EEG. However, because i-EEG requires surgery and the long-term efficacy for BCIs must still be explored, this approach is still not an option for patients. In order to improve s-EEG BCI performance, it is important understand the underlying brain phenomena and exploit the relationships between the s-EEG and generally superior i-BEG signals. Because the human head acts as a volume conductor consisting of the brain, cerebrospinal fluid, skull, and scalp tissue, linear mathematical models can be used to relate s-EEG and i-EEG. This dissertation presents unique s-EEG and i-EEG data that were recorded from the same subjects and used to develop novel empirical models to estimate s-EEG from i-EEG. These new empirical models can be used to better understand the sources and propagation of the relevant neural activity, as well as to validate existing theoretical volume conduction models. It is envisioned that this knowledge will help to advance algorithms for improving s-EEG BCI performance.
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
650 4 $a Computer engineering. $3 569006
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Old Dominion University. $3 1184212
773 0 $t Dissertation Abstracts International $g 76-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3581599 $z click for full text (PQDT)