Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications

Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study

Lower limb Movements' Classifications using Hemodynamic Response:fNIRS Study

Functional near-infrared spectroscopy (fNIRS) has become a viable approach for brain function investigation and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and resistance to electromagnetic noise. In this work, a hemodynamic response based on fNIRS signals was utilized to classify the right and left ankle joint movements. To achieve this objective, 32 optodes (emitters and detectors) were used to measure the hemodynamic responses in the motor cortex area during the motor execution task of the ankle joint movements. Two-channel sets were formed one including the channels directly related to the movement task, and another including all of the proposed channels. The results of this study reveal that the scheme based only on the selected channels outperformed the scheme that uses all channels. The classification accuracies were 91.38 % and 89.86 % respectively. These results demonstrated that fNIRS signal classification can be enhanced by eliminating the redundant channels

Multimodal Fusion Approach Based on EEG and EMG Signals for Lower Limb Movement Recognition

In this study, the fusion of cortical and muscular activities based on discriminant correlation analysis (DCA) is developed to recognize bilateral lower limb movements. Electromyography (EMG) and electroencephalography (EEG) signals were concurrently recorded from 28 healthy subjects while performing various ankle joint movements. The two types of biosignals were fused at feature level, and five different classifiers were used for the purpose of movement recognition. The performance of the classifiers with multimodal and single modality data were assessed with five different sampling window sizes. The results demonstrated that the use of a multimodal approach results in an improvement of the classification accuracy with a linear discriminator analysis classifier (LDA). The highest recognition accuracy was 96.64 ± 4.48% with a window size of 250 sample points, in contrast with 89.99 ± 7.94% for EEG data alone. Furthermore, the multimodal fusion based on DCA was validated with fatigued EMG signal to investigate the robustness of the fusion technique against the muscular fatigue. In addition, the statistical analysis result demonstrates that the proposed fusion approach provides a substantial improvement in motion recognition accuracy 96.64 ± 4.48% (p < 0.0001) compared to method based on a single modality

Detection of Lower Limb Movements using Sensorimotor Rhythms

In contrast to other brain imaging methods, electroencephalography (EEG) has become a feasible method for investigating brain activity and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and high temporal resolution. In this work, sensorimotor rhythms (SMR) signal was utilized to classify ankle joint movements. To achieve this goal the EEG signal in the motor cortex area was measured using 21 electrodes during the motor execution task of ankle joint movements. The event-related (de)synchronization (ERD/ ERS) technique was utilized to quantify the event-related in relation to EEG power changes. Inter and intralimb ankle movements were detected and classified The results show interlimb movements can be recognized better than intralimb movements. Where the average classification accuracy of the interlimb movements was 89.44 +/- 10.26% and 84.83 +/- 13.65% for the intralimb movements

Decoding the User's Movements Preparation From EEG Signals Using Vision Transformer Architecture

Electroencephalography (EEG) signals have a major impact on how well assistive rehabilitation devices work. These signals have become a common technique in recent studies to investigate human motion functions and behaviors. However, incorporating EEG signals to investigate motor planning or movement intention could benefit all patients who can plan motion but are unable to execute it. In this paper, the movement planning of the lower limb was investigated using EEG signal and bilateral movements were employed, including dorsiflexion and plantar flexion of the right and left ankle joint movements. The proposed system uses Continuous Wavelet Transform (CWT) to generate a time-frequency (TF) map of each EEG signal in the motor cortex and then uses the extracted images as input to a deep learning model for classification. Deep Learning (DL) models are created based on vision transformer architecture (ViT) which is the state-of-the-art of image classification and also the proposed models were compared with residual neural network (ResNet). The proposed technique reveals a significant classification performance for the multiclass problem (p < 0.0001) where the classification accuracy was 97.33±1.86 % and the F score, recall and precision were 97.32±1.88 %, 97.30±1.90 % and 97.36±1.81 % respectively. These results show that DL is a promising technique that can be applied to investigate the user's movements intention from EEG signals and highlight the potential of the proposed model for the development of future brain-machine interface (BMI) for neurorehabilitation purposes