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This study identifies an optimal set of 13 physiological features and the K-Nearest Neighbors (KNN) algorithm for accurate real-time affective state estimation, improving both classification accuracy and speed.

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Area of Science:

  • Psychophysiology
  • Machine Learning
  • Affective Computing

Background:

  • Affective states are psycho-physiological constructs linking mental and physiological processes, often modeled using arousal and valence.
  • Extracting affective states from physiological signals is challenging due to the lack of optimal feature sets and efficient classification methods.
  • Real-time estimation of affective states requires reliable and computationally efficient approaches.

Purpose of the Study:

  • To define a reliable and efficient approach for real-time affective state estimation.
  • To identify an optimal set of physiological features for affective state classification.
  • To determine the most effective machine learning algorithm for both binary and multi-class affective state estimation.

Main Methods:

  • Utilized the ReliefF feature selection algorithm to reduce 23 physiological features to an optimal set of 13.
  • Implemented and compared supervised learning algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA).
  • Tested the approach on physiological signals from 20 volunteers exposed to the International Affective Picture System (IAPS) stimuli.

Main Results:

  • The ReliefF algorithm successfully reduced the feature set from 23 to 13, enhancing classification performance.
  • Both accuracy and estimation time improved significantly with the use of the optimal feature set.
  • The K-Nearest Neighbors (KNN) algorithm demonstrated superior performance for real-time affective state estimation compared to SVM and LDA.

Conclusions:

  • The combination of the 13 identified optimal physiological features and the KNN classifier provides an effective approach for real-time affective state estimation.
  • This method offers improved accuracy and reduced estimation time, addressing limitations in current literature.
  • The findings support the use of KNN with selected physiological features for practical applications in affective computing.