Cargando…

Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection

BACKGROUND: Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Cao, Lei, Fan, Chunjiang, Wang, Zijian, Hou, Lusong, Wang, Haoran, Li, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369106/
https://www.ncbi.nlm.nih.gov/pubmed/32364149
http://dx.doi.org/10.3233/THC-209017
_version_ 1783560727613669376
author Cao, Lei
Fan, Chunjiang
Wang, Zijian
Hou, Lusong
Wang, Haoran
Li, Gang
author_facet Cao, Lei
Fan, Chunjiang
Wang, Zijian
Hou, Lusong
Wang, Haoran
Li, Gang
author_sort Cao, Lei
collection PubMed
description BACKGROUND: Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states. OBJECTIVE: We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI. METHODS: We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems. RESULTS: The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system. CONCLUSION: In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods.
format Online
Article
Text
id pubmed-7369106
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher IOS Press
record_format MEDLINE/PubMed
spelling pubmed-73691062020-07-22 Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection Cao, Lei Fan, Chunjiang Wang, Zijian Hou, Lusong Wang, Haoran Li, Gang Technol Health Care Research Article BACKGROUND: Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states. OBJECTIVE: We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI. METHODS: We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems. RESULTS: The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system. CONCLUSION: In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods. IOS Press 2020-06-04 /pmc/articles/PMC7369106/ /pubmed/32364149 http://dx.doi.org/10.3233/THC-209017 Text en © 2020 – IOS Press and the authors. All rights reserved https://creativecommons.org/licenses/by-nc/4.0/ This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).
spellingShingle Research Article
Cao, Lei
Fan, Chunjiang
Wang, Zijian
Hou, Lusong
Wang, Haoran
Li, Gang
Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection
title Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection
title_full Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection
title_fullStr Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection
title_full_unstemmed Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection
title_short Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection
title_sort alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of ssvep detection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369106/
https://www.ncbi.nlm.nih.gov/pubmed/32364149
http://dx.doi.org/10.3233/THC-209017
work_keys_str_mv AT caolei alertnessbasedsubjectdependentandsubjectindependentfilteroptimizationforimprovingclassificationefficiencyofssvepdetection
AT fanchunjiang alertnessbasedsubjectdependentandsubjectindependentfilteroptimizationforimprovingclassificationefficiencyofssvepdetection
AT wangzijian alertnessbasedsubjectdependentandsubjectindependentfilteroptimizationforimprovingclassificationefficiencyofssvepdetection
AT houlusong alertnessbasedsubjectdependentandsubjectindependentfilteroptimizationforimprovingclassificationefficiencyofssvepdetection
AT wanghaoran alertnessbasedsubjectdependentandsubjectindependentfilteroptimizationforimprovingclassificationefficiencyofssvepdetection
AT ligang alertnessbasedsubjectdependentandsubjectindependentfilteroptimizationforimprovingclassificationefficiencyofssvepdetection