Orthogonal semi-supervised regression with adaptive label dragging for cross-session EEG emotion recognition

Abstract

Owning to its merits of great temporal resolution, portability and low cost, electroencephalogram (EEG) signals have received increasing attention in emotion recognition. All the time, least square regression (LSR) has been widely employed in classification tasks. However, there exist two issues based on the LSR method, which limits its performance. The first problem is that the LSR method usually cannot make EEG data retain more discriminative information. The second is that it is improper for multiple emotion classification to use hard discrete labels as regression objectives. To address these issues, we develop orthogonal semi-supervised regression with adaptive label dragging model (OSRLD) to recognize emotions. OSRLD can get a closed-form solution with less computational costs. Furthermore, experimental findings using the open SEED-IV dataset show that 1) Compared to seven methods in terms of classification accuracy, OSRLD achieves the best average accuracy of 77.96%, 80.20% and 81.45%, 2) A technology of label dragging which modifies the label vector of each sample can effectively enlarge the distance between classes, and 3) Based on learned transformation matrix, the primary EEG frequency bands and brain areas are automatically identified, which lays theoretical basis for simplifying the hardware design of EEG acquisition devices.

Keywords

• Electroencephalogram (EEG)
• Emotion recognition
• Label dragging
• Orthogonal semi-supervised regression