  In human contact, emotion is very crucial. Attributes like words, voice
intonation, facial expressions, and kinesics can all be used to portray one's
feelings. However, brain-computer interface (BCI) devices have not yet reached
the level required for emotion interpretation. With the rapid development of
machine learning algorithms, dry electrode techniques, and different real-world
applications of the brain-computer interface for normal individuals, emotion
categorization from EEG data has recently gotten a lot of attention.
Electroencephalogram (EEG) signals are a critical resource for these systems.
The primary benefit of employing EEG signals is that they reflect true emotion
and are easily resolved by computer systems. In this work, EEG signals
associated with good, neutral, and negative emotions were identified using
channel selection preprocessing. However, researchers had a limited grasp of
the specifics of the link between various emotional states until now. To
identify EEG signals, we used discrete wavelet transform and machine learning
techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN)
algorithm. Initially, the classifier methods were utilized for channel
selection. As a result, final feature vectors were created by integrating the
features of EEG segments from these channels. Using the RNN and kNN algorithms,
the final feature vectors with connected positive, neutral, and negative
emotions were categorized independently. The classification performance of both
techniques is computed and compared. Using RNN and kNN, the average overall
accuracies were 94.844 % and 93.438 %, respectively.
