ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection

TL;DR

This paper introduces ASLSL, a novel method for feature selection in incomplete multi-modal physiological data for emotion recognition, effectively handling missing data and improving performance over existing methods.

Contribution

The paper proposes a new adaptive shared latent structure learning approach that addresses incomplete multi-modal data in emotion recognition, a challenge not tackled by prior methods.

Findings

ASLSL outperforms 17 existing feature selection methods on DEAP and DREAMER datasets.

ASLSL effectively mitigates the impact of missing data in multi-modal physiological signals.

Experimental results demonstrate the robustness and superiority of ASLSL in emotion classification tasks.

Abstract

Recently, multi-modal physiological signals based emotion recognition has garnered increasing attention in the field of brain-computer interfaces. Nevertheness, the associated multi-modal physiological features are often high-dimensional and inevitably include irrelevant, redundant, and noisy representation, which can easily lead to overfitting, poor performance, and high computational complexity in emotion classifiers. Feature selection has been widely applied to address these challenges. However, previous studies generally assumed that multi-modal physiological data are complete, whereas in reality, the data are often incomplete due to the openness of the acquisition and operational environment. For example, a part of samples are available in several modalities but not in others. To address this issue, we propose a novel method for incomplete multi-modal physiological signal feature selection called adaptive shared latent structure learning (ASLSL). Based on the property that similar features share similar emotional labels, ASLSL employs adaptive shared latent structure learning to explore a common latent space shared for incomplete multi-modal physiological signals and multi-dimensional emotional labels, thereby mitigating the impact of missing information and mining consensus information. Two most popular multi-modal physiological emotion datasets (DEAP and DREAMER) with multi-dimensional emotional labels were utilized to compare the performance between compare ASLSL and seventeen feature selection methods. Comprehensive experimental results on these datasets demonstrate the effectiveness of ASLSL.