Abstract:
"Electroencephalography (EEG) is a promising way to study brain activity and is being used to
figure out how people’s emotions work, which is important in fields like neurological and mental
health examinations. But it's hard for standard machine learning methods to understand the
complicated, high-dimensional data that EEG signals. This research looks at how well a
mechanism that combines Long Short Term Memory (LSTMs) Convolutional Neural Networks
(CNNs) can help classify emotions from EEG data. The author was aware of the problems with
only using CNN-based methods, especially when dealing with the changing timing of EEG data.
To solve these problems, the author came up with a novel approach that uses CNNs to pull out
spatial features and LSTMs to record the temporal sequences that go along with changes in
emotions. When these architectures work together, they try to describe both the location and timing
of brain activity that is connected to emotional states.
Used benchmark EEG emotion dataset to test our model and see how well it did compared
to normal CNN and LSTM models. Our tests showed that the hybrid model did better than these
traditional methods, showing more accurate mood classification. Also saw that fine-tuning the
hyperparameters could make the model much better at making predictions. Our findings show that
combining CNNs and LSTMs has a lot of potential for making EEG-based mood classification
systems that are more accurate and reliable. This study adds to the current conversation in affective
computing and makes the groundwork for further progress in the area in the future.
Keywords: EEG, Emotion Classification, Convolutional Neural Networks, Long Short Term
Memory, Affective Computing
Subject Descriptors:
• Neuroscience → Affective Computing → EEG Analysis
Computer Science → Machine Learning → Deep Learning → Hybrid Model"
