Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning
This work addresses brain-computer interface applications by improving EEG classification for understanding neural activity during cognitive tasks, but it is incremental as it applies existing deep learning methods to a specific dataset.
The paper tackled the problem of classifying EEG signals during mathematical calculations versus before calculations, achieving 99.72% accuracy using entropy features and a ConvLSTM classifier.
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.