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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Veröffentlicht in:PloS one Jg. 16; H. 8; S. e0253383
Hauptverfasser: Almarri, Badar, Rajasekaran, Sanguthevar, Huang, Chun-Hsi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco Public Library of Science 26.08.2021
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Analysis
Arousal
Benchmarks
Biology and Life Sciences
Brain research
Channels
Cognition & reasoning
Communication
Computer and Information Sciences
Computer science
Data mining
Datasets
EEG
Electrodes
Electroencephalography
Emotion recognition
Emotions
Engineering and Technology
Feature extraction
Feature selection
Human-computer interface
Information management
Interfaces
Learning
Machine learning
Medicine and Health Sciences
Neurophysiology
Outliers (statistics)
Physical Sciences
Physiology
Research and Analysis Methods
Social Sciences
Support vector machines
Temporal resolution
Time series
United States
United States > US
ISSN:1932-6203, 1932-6203
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Beschreibung
Zusammenfassung: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.
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Competing Interests: No, authors have no competing interests.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0253383