Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN

TL;DR

This study employs EEG signals and machine learning techniques, specifically RNN and kNN, to classify human emotions into positive, neutral, and negative categories with high accuracy, advancing emotion recognition technology.

Contribution

The paper introduces a novel approach combining channel selection, wavelet transform, and machine learning for improved EEG-based emotion classification.

Findings

RNN achieved 94.84% accuracy in emotion classification.

kNN achieved 93.44% accuracy in emotion classification.

The proposed method effectively distinguishes between different emotional states.

Abstract

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.