Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning

Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by “enhancing the difficulty of imagination tasks,” conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (−1.827) is less than that of LFMI (−1.3487) in the alpha band, so did (−3.4756 < −2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different ( p = 0.0074 < 0.01 ; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different ( p = 0.0034 < 0.01 ; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.

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“…The SVM is employed to calculate the recognition rate of the two task modes, namely, HFMI and LFMI as follows [ 26 – 29 ]:

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Motor Imagination of Lower Limb Movements at Different Frequencies

Liu

Chen

Belkacem

et al. 2021

Journal of Healthcare Engineering

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“…If an MI task-related is identified, a second phase follows the first phase by classifying it. Similarly, both Cheng et al [ 162 ] and Antelis et al [ 163 ] proposed a deep learning method that is trained to distinguish between resting state, transition state, and execution state. However, Cheng proposed a convolutional neural network, followed by a fully connected network (CNN-FC), while Antelis proposed Dendrite morphological neural networks (DMNN).…”

Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

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Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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“…The identification of the reference signals (R) remains the main disadvantage of such approach and should be well done during the training process and updated even during the test process. In [21], Patcharin et al, proposed a deep learning approach with a joint training scheme to recognize and track the pure imagery and non-pure imagery EEG signals. A channel selection is peformed manually by selecting only three electrodes C 3 , C z and C 4 allowing to reach a mean classification accuracy beyond 71% and 70% using, respectively, the CNN-FC and the CSP with SVM algorithms.…”

Section: Figure 1: Typical Eeg Signal Processing Chainmentioning

confidence: 99%

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

2020

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Brain-computer interface (BCI) acquires, analyzes and transforms human brain activity to control commands allowing as such disabled people to communicate or control external devices. A motor imagery-based BCI enables patients to control artificial peripherals and communicate with the outside world by merely thinking of the task such as, e.g., the imagination of left-hand, right-hand, or foot movement. The mere intention of moving one of the limbs triggers neural activity, which is induced in the primary sensorimotor areas like that observed with real executed movements. Tracking generated sensorimotor rhythms (SMRs) and extracting robust and informative features from electroencephalogram (EEG) signals are challenging due to the time-varying nature of EEG signals and the inter-human variability. In this paper, we proposed an EEG-zeros-time windowing (E2ZTW) approach based on a highly decaying window function to track SMRs and identify the temporal epochs containing useful information without any prior information on the trigger. The proposed approach involves the application of the group-delay function, allowing the improvement of the spectral resolution due to the additive property of the function on individual resonances. Some algorithms were integrated into the proposed approach, such as the common spatial pattern algorithm, which is used to extract features and linear discriminant analysis and a convolutional neural network, which are used for the classification of the features. The effectiveness of the proposed approach in tracking the SMRs rhythms is evaluated in terms of accuracy. Experiments were performed on three public datasets provided by BCI competition for 17 subjects. Following experimental results, it is shown that discrimination between the left-and right-hand movements can be achieved within a few seconds with high classification accuracy. As compared to other state-of-art techniques, the proposed approach achieves an average classification accuracy and standard error values of 82% and 13, respectively, thereby outperforming existing algorithm by an accuracy mean of 2%.

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Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning

Cited by 7 publications

References 9 publications

Motor Imagination of Lower Limb Movements at Different Frequencies

Motor Imagination of Lower Limb Movements at Different Frequencies

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

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