  Classifying Electroencephalogram(EEG) signals helps in understanding
Brain-Computer Interface (BCI). EEG signals are vital in studying how the human
mind functions. In this paper, we have used an Arithmetic Calculation dataset
consisting of Before Calculation Signals (BCS) and During Calculation Signals
(DCS). The dataset consisted of 36 participants. In order to understand the
functioning of neurons in the brain, we classified BCS vs DCS. For this
classification, we extracted various features such as Mutual Information (MI),
Phase Locking Value (PLV), and Entropy namely Permutation entropy, Spectral
entropy, Singular value decomposition entropy, Approximate entropy, Sample
entropy. The classification of these features was done using RNN-based
classifiers such as LSTM, BLSTM, ConvLSTM, and CNN-LSTM. The model achieved an
accuracy of 99.72% when entropy was used as a feature and ConvLSTM as a
classifier.
