TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification of EEG signals for facial expression and motor execution with deep learning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification of EEG signals for facial expression and motor execution with deep learning

BCI
Deep learning
EEG
Nueral network
PCA

Recently, algorithms of machine learning are widely used with the field of
electroencephalography (EEG) brain-computer interfaces (BCI). The
preprocessing stage for the EEG signals is performed by applying the
principle component analysis (PCA) algorithm to extract the important
features and reducing the data redundancy. A model for classifying EEG,
time series, signals for facial expression and some motor execution processes
had been designed. A neural network of three hidden layers with deep
learning classifier had been used in this work. Data of four different subjects
were collected by using a 14 channels Emotiv EPOC+ device. EEG dataset
samples including ten action classes for the facial expression and some motor
execution movements are recorded. A classification results with accuracy
range (91.25-95.75%) for the collected samples were obtained with respect
to: number of samples for each class, total number of EEG dataset samples
and type of activation function within the hidden and the output layer
neurons. A time series EEG signal was taken as signal values not as image or
histogram, analysed and classified with deep learning to obtain the satisfied
results of accuracy.

Areej Hameed Al-Anbary, Salih Al-Qaraawi

http://journal.uad.ac.id/index.php/TELKOMNIKA

Jun 28, 2021

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