The detection of emotions using an Electroencephalogram (EEG) is a crucial
area in brain-computer interfaces and has valuable applications in fields such
as rehabilitation and medicine. In this study, we employed transfer learning to
overcome the challenge of limited data availability in EEG-based emotion
detection. The base model used in this study was Resnet50. Additionally, we
employed a novel feature combination in EEG-based emotion detection. The input
to the model was in the form of an image matrix, which comprised Mean Phase
Coherence (MPC) and Magnitude Squared Coherence (MSC) in the upper-triangular
and lower-triangular matrices, respectively. We further improved the technique
by incorporating features obtained from the Differential Entropy (DE) into the
diagonal, which previously held little to no useful information for classifying
emotions. The dataset used in this study, SEED EEG (62 channel EEG), comprises
three classes (Positive, Neutral, and Negative). We calculated both
subject-independent and subject-dependent accuracy. The subject-dependent
accuracy was obtained using a 10-fold cross-validation method and was 93.1%,
while the subject-independent classification was performed by employing the
leave-one-subject-out (LOSO) strategy. The accuracy obtained in
subject-independent classification was 71.6%. Both of these accuracies are at
least twice better than the chance accuracy of classifying 3 classes. The study
found the use of MSC and MPC in EEG-based emotion detection promising for
emotion classification. The future scope of this work includes the use of data
augmentation techniques, enhanced classifiers, and better features for emotion
classification.

本研究使用 Resnet50 作为基本模型和 Mean Phase Coherence（MPC）和 Magnitude Squared Coherence（MSC）的组合来识别情绪，结果表明该方法在情感分类方面很有前途。