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An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data by Mahnoosh Tajmirriahi; Zahra Amini; Hossein Rabbani; Rahele Kafieh is a Engineering article available to read on EtoBox.

What is An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data about?

In this study, a new deep learning-based methodology is developed for P300 detection in brain computer interface (BCI) systems based on time-frequency (TF) features of EEG signals coupled to deep learning. The TF distributions can transform EEG signals to the TF images by simultaneous representation of time and frequency properties of the signal. However, they do not display the energy distribution of signals at different scales identically and their advantages may be greatly incorporated by using them together. Here, four TF images of single-trail EEG signal are computed and the concatenation of the TF (cTF) images of each signal is developed to be used as training data for a simple and lightweight deep learning-based classifier. The applied TF distributions are spectrogram, Wigner-Ville distribution, Morlet-scalogram, and Bertrand distribution. Performance of method is evaluated over limited data acquired from the normal and amyotrophic lateral sclerosis (ALS) datasets and accuracy of 96.56%, and 96.84% are achieved respectively, which is superior to the other comparing algorithms. Moreover, results of cross-subject classification indicate the promising ability of the method in e

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Author

Mahnoosh Tajmirriahi; Zahra Amini; Hossein Rabbani; Rahele Kafieh

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)