Piyush Kant1, Shahedul Haque Laskar2, Jupitara Hazarika2, Rupesh Mahamune2. 1. Department of Electronics and Instrumentation Engineering, National Institute of Technology, Silchar 788010, Assam, India. Electronic address: piyushtwri@gmail.com. 2. Department of Electronics and Instrumentation Engineering, National Institute of Technology, Silchar 788010, Assam, India.
Abstract
BACKGROUND: The processing of brain signals for Motor imagery (MI) classification to have better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional methods like Artificial neural network (ANN), Linear discernment analysis (LDA), K-Nearest Neighbor (KNN), Support vector machine (SVM), etc. have made significant progress in terms of classification accuracy, deep transfer learning-based systems have shown the potential to outperform them. BCI can play a vital role in enabling communication with the external world for persons with motor disabilities. NEW METHODS: Deep learning has been a success in many fields. However, for Electroencephalogram (EEG) signals, relatively minimal work has been carried out using deep learning. This paper proposes a combination of Continuous Wavelet Transform (CWT) along with deep learning-based transfer learning to solve the problem. CWT transforms one dimensional EEG signals into two-dimensional time-frequency-amplitude representation enabling us to exploit available deep networks through transfer learning. RESULTS: The effectiveness of the proposed approach is evaluated in this study using an openly available BCI competition data-set. The results of the approach have been compared to earlier works on the same dataset, and a promising validation accuracy of 95.71% is achieved in our investigation. COMPARISON WITH EXISTING METHODS AND CONCLUSION: Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.
BACKGROUND: The processing of brain signals for Motor imagery (MI) classification to have better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional methods like Artificial neural network (ANN), Linear discernment analysis (LDA), K-Nearest Neighbor (KNN), Support vector machine (SVM), etc. have made significant progress in terms of classification accuracy, deep transfer learning-based systems have shown the potential to outperform them. BCI can play a vital role in enabling communication with the external world for persons with motor disabilities. NEW METHODS: Deep learning has been a success in many fields. However, for Electroencephalogram (EEG) signals, relatively minimal work has been carried out using deep learning. This paper proposes a combination of Continuous Wavelet Transform (CWT) along with deep learning-based transfer learning to solve the problem. CWT transforms one dimensional EEG signals into two-dimensional time-frequency-amplitude representation enabling us to exploit available deep networks through transfer learning. RESULTS: The effectiveness of the proposed approach is evaluated in this study using an openly available BCI competition data-set. The results of the approach have been compared to earlier works on the same dataset, and a promising validation accuracy of 95.71% is achieved in our investigation. COMPARISON WITH EXISTING METHODS AND CONCLUSION: Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.
Authors: Areej A Malibari; Fahd N Al-Wesabi; Marwa Obayya; Mimouna Abdullah Alkhonaini; Manar Ahmed Hamza; Abdelwahed Motwakel; Ishfaq Yaseen; Abu Sarwar Zamani Journal: J Healthc Eng Date: 2022-03-24 Impact factor: 2.682