PurposeThe purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.Design/methodology/approachClassical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.FindingsThe two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.Originality/valueThe present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

Emotion recognition using electroencephalogram (EEG) signals is an aspect of affective computing. The EEG refers to recording brain responses via electrical signals by showing external stimuli to the participants. This paper proposes the prediction of valence, arousal, dominance and liking for EEG signals using a deep neural network (DNN). The EEG data is obtained from the AMIGOS dataset, a publicly available dataset for mood and personality research. Two features, normalized and power and normalized wavelet energy, are extracted using Fourier and wavelet transform, respectively. A DNN with three different activation functions (exponential linear unit, rectified linear unit [ReLU] and leaky ReLU) has been applied for single and combined features. The result of combined features with leaky ReLU is found to be the best, with a classification accuracy of 85.47, 81.87, 84.04 and 86.63 for valence, arousal, dominance and liking, respectively.