Emotion Recognition Using Neighborhood Components Analysis and ECG/HRV-Based Features

Previous research showed that supervised dimensionality reduction
using Neighborhood Components Analysis (NCA) enhanced the performance of
3-class problem emotion recognition using ECG only where features were the
statistical distribution of dominant frequencies and the first differences after
applying bivariate empirical mode decomposition (BEMD). This paper explores
how much NCA enhances emotion recognition using ECG-derived features,
esp. standard HRV features with two difference normalization methods and
statistical distribution of instantaneous frequencies and the first differences
calculated using Hilbert-Huang Transform (HHT) after empirical mode
decomposition (EMD) and BEMD. Results with the MAHNOB-HCI database
were validated using subject-dependent and subject-independent scenarios with
kNN as classifier for 3-class problem in valence and arousal. A t-test was used to
assess the results with significance level 0.05. Results show that NCA enhances
the performance up to 74% from the implementation without NCA with
p-values close to zero in most cases. Different feature extraction methods offered
different performance levels in the baseline but the NCA enhanced them such
that the performances were close to each other. In most experiments use of
combined standardized and normalized HRV-based features improved performance.
Using NCA on this database improved the standard deviation significantly
for HRV-based features under subject-independent scenario.