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- W2797419573 abstract "In the present work, we aim to classify human emotional states categorized based on the arousal-valence model [1] by applying logistic regression (the original and L 1 — regularized LR model) to nonlinear features extracted from electroencephalographic (EEG) signals. Recurrence quantification analysis (RQA) [2] was employed to effectively capture the underlying dynamics behind the complex reactivity corresponding to affective phenomenon. A benchmark dataset, DEAP [3], was used for our two-fold objectives: (1) to investigate the suitability of RQA measures and regularized learning method for emotion recognition, and (2) to compare the performances as well as topographic patterns of important channels for classifying emotional states with previous studies. The results demonstrated that our proposed method with selected RQA measures has better performance (test accuracy = 75.7% and F1 score = 78.1% on average) comparing to previous studies, and Li-regularized model is less over-fitted comparing to the LR." @default.
- W2797419573 created "2018-04-24" @default.
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- W2797419573 date "2018-03-01" @default.
- W2797419573 modified "2023-09-27" @default.
- W2797419573 title "Recognizing affective state patterns using regularized learning with nonlinear dynamical features of EEG" @default.
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- W2797419573 doi "https://doi.org/10.1109/bhi.2018.8333388" @default.
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