2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Palembang, Endonezya, 20 - 21 Eylül 2023, ss.49-53
Deep learning techniques can recognize and learn significant features
automatically. The advantages of deep learning should also be used to
solve problems in other fields. Therefore, we concentrated on
classifying motor imagery EEG signals with deep transfer learning. The
present study first proposed an EEG signal-to-image conversion method
that uses 2D signal representations. Thanks to this, we employed 2D
images as input instead of standard one-dimensional EEG features.
Second, we used AlexNet, GoogLeNet, and SqueezeNet pre-trained
convolutional neural network frameworks for classification. We also used
hard voting and merged the outputs of pre-trained convolutional neural
networks. We performed experiments on dataset 2a of BCI Competition IV,
achieving 84.18±5.37% classification accuracy and 0.66±0.12 kappa across
nine subjects, even with limited training data. The results showed that
the proposed signal-to-image conversion method with deep transfer
learning has potential, particularly in processing biomedical signals.