Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.

The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, trans...

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Autores principales:	Badar Almarri, Sanguthevar Rajasekaran, Chun-Hsi Huang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b153b040d3fa4a59a4b8f128d6a55bd8
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id	oai:doaj.org-article:b153b040d3fa4a59a4b8f128d6a55bd8
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spelling	oai:doaj.org-article:b153b040d3fa4a59a4b8f128d6a55bd82021-12-02T20:17:35ZAutomatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.1932-620310.1371/journal.pone.0253383https://doaj.org/article/b153b040d3fa4a59a4b8f128d6a55bd82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253383https://doaj.org/toc/1932-6203The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%-27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition.Badar AlmarriSanguthevar RajasekaranChun-Hsi HuangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0253383 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Badar Almarri Sanguthevar Rajasekaran Chun-Hsi Huang Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.
description	The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%-27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition.
format	article
author	Badar Almarri Sanguthevar Rajasekaran Chun-Hsi Huang
author_facet	Badar Almarri Sanguthevar Rajasekaran Chun-Hsi Huang
author_sort	Badar Almarri
title	Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.
title_short	Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.
title_full	Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.
title_fullStr	Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.
title_full_unstemmed	Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.
title_sort	automatic subject-specific spatiotemporal feature selection for subject-independent affective bci.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b153b040d3fa4a59a4b8f128d6a55bd8
work_keys_str_mv	AT badaralmarri automaticsubjectspecificspatiotemporalfeatureselectionforsubjectindependentaffectivebci AT sanguthevarrajasekaran automaticsubjectspecificspatiotemporalfeatureselectionforsubjectindependentaffectivebci AT chunhsihuang automaticsubjectspecificspatiotemporalfeatureselectionforsubjectindependentaffectivebci
_version_	1718374367262408704

Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.

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