TITLE:
Treatment of Imbalance Dataset for Human Emotion Classification
AUTHORS:
Er. Shrawan Thakur
KEYWORDS:
Electroencephalography (EEG), Brain Computer Interface (BCI), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Neural Network (NN), Synthetic Minority Over Sampling Technique (SMOTE)
JOURNAL NAME:
World Journal of Neuroscience,
Vol.13 No.4,
November
6,
2023
ABSTRACT: Developments in biomedical science, signal processing technologies have
led Electroencephalography (EEG) signals to be widely used in the diagnosis of
brain disease and in the field of Brain-Computer Interface (BCI). The collected
EEG signals are processed using Machine Learning-Random Forest and Naive Bayes- and Deep Learning-Recurrent
Neural Network (RNN), Neural Network (NN) and Long Short Term Memory
(LSTM)-Algorithms to obtain the recent mood of a person. The Algorithms
mentioned above have been imposed on the data set in order to find out what the
person is feeling at a particular moment. The following thesis is conducted to
find out one of the following moods (happy, surprised, disgust, fear, anger and
sadness) of a person at an instant, with an aim to obtain the result with least
amount of time delay as the mood differs. It is pretty obvious that the
accuracy of the output varies depending upon the algorithm used, time taken to
process the data, so that it is easy for us to compare the reliability and
dependency of a particular algorithm to another, prior to its practical
implementation. The imbalance data sets that were used had an imbalanced class
and thus, over fitting occurred. This problem was handled by generating
Artificial Data sets with the use of SMOTE Oversampling Technique.