An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees. (July 2021)

• Record Type: Journal Article
• Authors: Idowu, Oluwagbenga Paul; Ilesanmi, Ademola Enitan; Li, Xiangxin; Samuel, Oluwarotimi Williams; Fang, Peng; Li, Guanglin
• Is Part Of: Computer methods and programs in biomedicine. Volume 206(2021)
• Publication Date: 2021-07
• Subjects: Brain computer interface (BCI) -- Electroencephalography (EEG) -- Motor intention (MI) -- Long short-term memory (LSTM) -- Stacked Autoencoder (SAE) -- t-distributed stochastic neighbor embedding (t-SNE); Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106121
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Proposed an integrated deep learning model for recognition of multiple classes of motor intention tasks from raw EEG signals acquired from trans-humeral amputees without any pre-feature extraction. Pre-processed raw EEG signals in two stages; to remove artifacts and other related noises, and to select the desired frequency bands. Task-related frequency components were introduced as input representation to the proposed model. A non-linear dimensionality reduction technique called "t-SNE" was applied to the raw EEG data. The proposed LSTM-SAE model performed better than the state-of-the-art methods, and also achieved an outstanding overall performance when compared with conventional methods. The outcome of this study offer a potential application in the development of a more intuitive and naturalistic prosthetic control for amputees with severe motor disabilities. Abstract: Background and objective: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions. Methods: The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition. Results: The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space. Conclusion: This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.