Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques

Abstract

In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual's EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.

Keywords: Artificial Bee colony; Convolutional neural network; Electroculogram; Electroencephalogram; Grey Wolf Optimizer; Hybrid learning methods.

Fig. 1

(a) Electrodes Position (b) Two-dimensional valence arousal space of emotion.

Fig. 2

Hybrid ABC-GWO algorithm workflow.

Fig. 3

LSTM Architectural Design.

Fig. 4

LSTM framework and Summary.

Fig. 5

Proposed CNN-LSTM Workflow.

Fig. 6

Proposed framework of CNN + LSTM.

Fig. 7

RNN Framework and Summary.

Fig. 8

Proposed CNN-ABC-GWO workflow.

Fig. 9

CNN – (a) Model loss and (b) Model Accuracy.

Fig. 10

CNN + LSTM – (a) Model loss and (b) Model Accuracy.

Fig. 11

RNN – (a) Model loss and (b) Model Accuracy.

Fig. 12

CNN + ABC + GWO – (a) Model loss and (b) Model Accuracy.

Fig. 13

ROC Curve for the proposed CNN + ABC + GWO.

Fig. 14

Accuracy comparison of the Proposed Models.

Fig. 15

Confusion Matrix (a) CNN (b) CNN + LSTM (c) RNN + SAE (d) SAE + CNN + LSTM.