Sparse Coding Techniques with Deep Neural Network for EEG Auto Encoding: Enhancing Feature Representation Efficiency

Research on emotion classification using Brain-Computer Interface (BCI) systems is an intriguing aspect. Deep learning has recently been used to classify emotions in BCI systems, and better results have been seen when compared to more conventional classification techniques. Although the high dimensi...

Full description

Saved in:
Bibliographic Details
Published in:International Conference on Recent Advances in Information Technology (Online) pp. 1 - 8
Main Authors: Rama, M., Nalini, T, Khyathi, P, Saikiran, N., Silpa, Ch
Format: Conference Proceeding
Language:English
Published: IEEE 06.03.2025
Subjects:
Autoencoders
Autoencoding
Deep learning
Electroencephalography
Encoding
Feature Learning
Feature Representation
Noise
Representation learning
Root mean square
Scalability
Signal analysis
Signal reconstruction
SparseCoding
ISSN:2994-287X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Research on emotion classification using Brain-Computer Interface (BCI) systems is an intriguing aspect. Deep learning has recently been used to classify emotions in BCI systems, and better results have been seen when compared to more conventional classification techniques. Although the high dimensionality and noise of Electroencephalography (EEG) signals make it difficult to research brain activity is essential for efficient feature representation. To improve the effectiveness of feature representation, describe a unique method in this paper for EEG autoencoding that combines Sparse Coding Techniques with Deep Neural Networks (SCT-DNN).. Using several encoding and decoding layers, the deep auto-encoder is intended to learn hierarchical representations that capture crucial aspects of the EEG signal. Promote efficient representation and reconstruction of EEG signals by enticing the machine to learn sparse and discriminative features through the use of sparse coding throughout the encoding phase. Results from the experiment demonstrate the advantages of SCT-DNN for learning EEG features and provide insights into the underlying brain processes. Through the synergistic use of deep learning and sparse coding, this proposed work optimizes the efficiency of feature representation, furthering the field of EEG signal analysis.
AbstractList Research on emotion classification using Brain-Computer Interface (BCI) systems is an intriguing aspect. Deep learning has recently been used to classify emotions in BCI systems, and better results have been seen when compared to more conventional classification techniques. Although the high dimensionality and noise of Electroencephalography (EEG) signals make it difficult to research brain activity is essential for efficient feature representation. To improve the effectiveness of feature representation, describe a unique method in this paper for EEG autoencoding that combines Sparse Coding Techniques with Deep Neural Networks (SCT-DNN).. Using several encoding and decoding layers, the deep auto-encoder is intended to learn hierarchical representations that capture crucial aspects of the EEG signal. Promote efficient representation and reconstruction of EEG signals by enticing the machine to learn sparse and discriminative features through the use of sparse coding throughout the encoding phase. Results from the experiment demonstrate the advantages of SCT-DNN for learning EEG features and provide insights into the underlying brain processes. Through the synergistic use of deep learning and sparse coding, this proposed work optimizes the efficiency of feature representation, furthering the field of EEG signal analysis.
Author Rama, M.
Khyathi, P
Nalini, T
Silpa, Ch
Saikiran, N.
Author_xml – sequence: 1
  givenname: M.
  surname: Rama
  fullname: Rama, M.
  email: meruvarama2004@gmail.com
  organization: Saveetha Institute of Medical and Technical Sciences(SIMATS), Saveetha University,Saveetha School of Engineering,Dept. of CSE,Chennai,India
– sequence: 2
  givenname: T
  surname: Nalini
  fullname: Nalini, T
  email: nalinit.sse@saveetha.com
  organization: Saveetha Institute of Medical and Technical Sciences(SIMATS), Saveetha University,Saveetha School of Engineering,Dept. of CSE,Chennai,India
– sequence: 3
  givenname: P
  surname: Khyathi
  fullname: Khyathi, P
  email: khyathipurini9@gmail.com
  organization: QIS College of Engineering and Technology,Dept. of AIMLDS,Ongole,India
– sequence: 4
  givenname: N.
  surname: Saikiran
  fullname: Saikiran, N.
  email: nlmsaikiran@gmail.com
  organization: Madanapalle Institute of Technology & Science, madanapalle (A),Dept. of CST,Andhra Pradesh,India
– sequence: 5
  givenname: Ch
  surname: Silpa
  fullname: Silpa, Ch
  email: silpa.ch@qiscet.edu.in
  organization: QIS College of Engineering and Technology,Dept. of IT,Ongole,India
BookMark eNo1kMtOAjEYhavRRETewMS-ANjLTNvfHcEBSYgmOAt3pNR_pIqdsdMJ4e0FL6vv5CTnW5xLchbqgITccDbinMHtcjwvVc6UGQkm8mNnDAh9QgagwUjJc5EZpk9JTwBkQ2H0ywUZtO07Y0xyrbVUPZKeGxtbpJP61Yc3WqLbBP_VYUt3Pm3oPWJDH7GLdntA2tXxg1Z1pEUxo-Mu1bQI7md5d0gbG9xRMkWbuoh0iU3EFkOyydeBFlXlncfg9lfkvLLbFgd_7JNyWpSTh-HiaTafjBdDDzINswwkWF6BYmCslNqpTOa45sgEOrQglDaZ46CMsBwEmrUVa5cbq7ECULJPrn-1HhFXTfSfNu5X_z_Jb5g9X24
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/RAIT65068.2025.11088927
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9798331524807
EISSN 2994-287X
EndPage 8
ExternalDocumentID 11088927
Genre orig-research
GroupedDBID 6IE
6IF
6IK
6IL
AAJGR
CBEJK
IPLJI
RIE
RIL
ID FETCH-LOGICAL-i93t-44939a1f96098a337c6435eb1e02ecea926784c19682a192e8ba2bc58a7ef9963
IEDL.DBID RIE
IngestDate Wed Aug 27 02:04:24 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i93t-44939a1f96098a337c6435eb1e02ecea926784c19682a192e8ba2bc58a7ef9963
PageCount 8
ParticipantIDs ieee_primary_11088927
PublicationCentury 2000
PublicationDate 2025-March-6
PublicationDateYYYYMMDD 2025-03-06
PublicationDate_xml – month: 03
  year: 2025
  text: 2025-March-6
  day: 06
PublicationDecade 2020
PublicationTitle International Conference on Recent Advances in Information Technology (Online)
PublicationTitleAbbrev RAIT
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003177736
Score 1.901129
Snippet Research on emotion classification using Brain-Computer Interface (BCI) systems is an intriguing aspect. Deep learning has recently been used to classify...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Autoencoders
Autoencoding
Deep learning
Electroencephalography
Encoding
Feature Learning
Feature Representation
Noise
Representation learning
Root mean square
Scalability
Signal analysis
Signal reconstruction
SparseCoding
Title Sparse Coding Techniques with Deep Neural Network for EEG Auto Encoding: Enhancing Feature Representation Efficiency
URI https://ieeexplore.ieee.org/document/11088927
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELVoxQALX0V844E1_bCd2GarSgpIqKpKhLpViXMRlVBStQm_n7ObFhgYmGJFdhTZSu6d771nQu6CVPJejGlq4GfgiZ5JvUSm4GlleMq6EoQrF7y9yNFITad6XIvVnRYGABz5DNq26Wr5aWEqu1XWsZR1pZlskIaUwVqstd1QwUAopS1F3tY-mp1J_zlCABJYBhfz25vRv85RcWFkePDPFzgkrW9BHh1vQ80R2YH8mOz_8BI8IeXrAnNUoIPCdqHRxpt1Re1WK30AWFDrxBF_4MVRvyniVRqGj7RflQUNc-NG3mPr3Zpw4EMsPqyWQCeOLlurlHIaOtsJq9lskWgYRoMnrz5SwZtrXnpCaK7jXmZt5lTMuTQISHz8XUOXgYFYM4xdwuBXqViM2A9UErPE-CqWkGFmxE9JMy9yOCM04cJPEFsJX2QCcaFWUuguS3WWSq0zfk5adv5mi7VpxmwzdRd_3L8ke3aVHL0ruCLNclnBNdk1n-V8tbxxS_0FJkGpwA
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwGG0UTdSLvzD-pgevw9F26-qN4BAiEoKL4Ua67lskMRuBzb_ftgzUgwdPa5Z2Wdps3_v6vfeK0J2fcNqSOk31vRQc1lKJE_MEHBEomhCXA7PlgrcBHw6DyUSMKrG61cIAgCWfQdM0bS0_yVVptsruDWU9EIRvox2PMeKu5FqbLRUdCjk3xchG5aR5P273Iw1BfMPhIl5zPf7XSSo2kHQP__kKR6j-LcnDo02wOUZbkJ2ggx9ugqeoeJ3rLBVwJzddcLR2Z11is9mKHwHm2HhxyA99seRvrBErDsMn3C6LHIeZsiMfdOvd2HDohxiEWC4Ajy1httIpZTi0xhNGtVlHUTeMOj2nOlTBmQlaOIwJKmQrNUZzgaSUKw1JPP3DBpeAAimIjl5M6e8yIFKjPwhiSWLlBZJDqnMjeoZqWZ7BOcIxZV6s0RXzWMo0MhQBZ8IliUgTLkRKL1DdzN90vrLNmK6n7vKP-w2014teBtNBf_h8hfbNilmyl3-NasWihBu0qz6L2XJxa5f9CyifrQc
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=International+Conference+on+Recent+Advances+in+Information+Technology+%28Online%29&rft.atitle=Sparse+Coding+Techniques+with+Deep+Neural+Network+for+EEG+Auto+Encoding%3A+Enhancing+Feature+Representation+Efficiency&rft.au=Rama%2C+M.&rft.au=Nalini%2C+T&rft.au=Khyathi%2C+P&rft.au=Saikiran%2C+N.&rft.date=2025-03-06&rft.pub=IEEE&rft.eissn=2994-287X&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FRAIT65068.2025.11088927&rft.externalDocID=11088927