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

• Published in: PloS one Vol. 16; no. 8; p. e0253383
• Main Authors: Almarri, Badar, Rajasekaran, Sanguthevar, Huang, Chun-Hsi
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 26.08.2021; Public Library of Science (PLoS)
• Subjects: 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
• DOI: 10.1371/journal.pone.0253383

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.