Recent advances in machine
learning have enabled the development of techniques to detect and recognize
human emotions. Some of these techniques work by analyzing
electroencephalography (EEG) signals, which are essentially recordings of the
electrical activity of the brain collected from a person's scalp. Most
EEG-based emotion classification methods introduced over the past decade or so
employ traditional machine learning (ML) techniques such as support vector
machine (SVM) models, as these models require fewer training samples and there
is still a lack of large-scale EEG datasets. Recently, however, researchers
have compiled and released several new datasets containing EEG brain
recordings. The release of new datasets opens exciting new possibilities for
EEG-based emotion recognition, as they could be used to train deep-learning
models that achieve better performance than traditional ML techniques.
Unfortunately, however, the low resolution of EEG signals contained in these
datasets could make training deep-learning models rather difficult.
To enhance the resolution of
available EEG data, researchers first generated so-called topology-preserving
differential entropy features using the electrode coordinates at the time when
the data was collected. Subsequently, they developed a convolutional neural
network (CNN) and trained it on the updated data, teaching it to estimate three
general classes of emotions (i.e., positive, neutral and negative). The
researchers trained and evaluated their approach on the SEED dataset, which
contains 62-channel EEG signals. They found that their method could classify
emotions with a remarkable average accuracy of 90.41 percent, outperforming
other machine-learning techniques for EEG-based emotion recognition. In the
future, the method proposed by researchers could inform the development of new EEG-based
emotion recognition tools, as it introduces a viable solution for overcoming
the issues associated with the low-resolution of EEG data. The same approach
could also be applied to other deep-learning models for the analysis of EEG
data.
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