Performance Evaluation of Xception Networks and Short-Time Fourier Transform Spectrograms for Motor Imagery Classification
Subject Areas : Image Processing
Masoud Sistaninezhad
1
,
Habib Rasi
2
*
,
Aliakbar Abdollahinezhadfard
3
1 - Seraj Institute of Higher Education, East Azarbaijan, Tabriz, Iran
2 - Sahand University of Technology, East Azarbaijan, New City of Sahand, Iran
3 - Tabriz University, Tabriz, Iran
Keywords: Motor Imagery, Xception Network, Convolutional Neural Network, Short-time Fourier Transform, Deep Transfer learning.,
Abstract :
Short-time Fourier transform (STFT) in classifying electroencephalogram (EEG) signals with a limited number of training samples, utilizing pre-trained deep transfer learning. While most deep learning research has primarily focused on one-dimensional time series inputs, utilizing two-dimensional inputs offers a promising approach for leveraging EEG signals in deep learning models. In this study, a novel two-dimensional STFT-based method was employed to transform EEG signals into images, which were then classified using the Xception model. The BCI Competition IV dataset 2b, consisting of EEG signals from nine participants, was utilized for performance evaluation. This dataset allowed for a comprehensive analysis of the proposed STFT+Xception approach for classifying motor imagery signals. Notably, this study is the first to report the results of this approach in such a context. The obtained results demonstrated the effectiveness of the STFT+Xception approach in classifying motor imagery signals with a limited number of EEG samples. The average classification accuracy exceeded 80% for all nine subjects, showcasing the robustness of the proposed method. Furthermore, the standard deviation across subjects was found to be remarkably low, measuring only 2.9%. These findings highlight the potential of the STFT+Xception approach for accurate and reliable classification of EEG signals, even with limited training data. Additionally, the study identified avenues for further improvement. Applying data augmentation techniques and training the model from scratch with augmented data may yield even more successful results in future experiments. This indicates the potential for enhancing the classification performance and expanding the applicability of the proposed approach to broader EEG datasets.
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