Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

Haya Aldawsari; Saad Al-Ahmadi; Farah Muhammad

doi:10.3390/diagnostics13162624

Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals

Diagnostics (Basel). 2023 Aug 8;13(16):2624. doi: 10.3390/diagnostics13162624.

Authors

Haya Aldawsari¹, Saad Al-Ahmadi^{2

3}, Farah Muhammad²

Affiliations

¹ Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.
² Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11543, Saudi Arabia.
³ College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Abstract

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning method, specifically one-dimensional convolutional neural networks (1D-CNN), to analyze EEG signals and classify emotional states. By capturing intricate patterns and relationships within the data, the 1D-CNN model accurately distinguished between emotional states (HV/LV and HA/LA). Moreover, an efficient method for data augmentation was used to increase the sample size and observe the performance deep learning model using additional data. The study conducted EEG-based emotion recognition tests on SEED, DEAP, and MAHNOB-HCI datasets. Consequently, this approach achieved mean accuracies of 97.6, 95.3, and 89.0 on MAHNOB-HCI, SEED, and DEAP datasets, respectively. The results have demonstrated significant potential for the implementation of a cost-effective IoT device to collect EEG signals, thereby enhancing the feasibility and applicability of the data.

Keywords: 1D-CNN; EEG; emotion recognition; human-computer interactions.

Grants and funding

The authors would like to thank Deanship of Scientific Research in King Saud University for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR).