Dimensionality Reduction for Classification of Object Weight from Electromyography

Electromyography (EMG) is a simple, non-invasive, and cost-effective technology for measuring muscle activity. However, multi-muscle EMG is also a noisy, complex, and high-dimensional signal. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and, in particular, to measure the reach-and-grasp motions of the human hand. Here, we developed an automated pipeline to predict object weight in a reach-grasp-lift task from an open dataset, relying only on EMG data. In doing so, we shifted the focus from manual feature-engineering to automated feature-extraction by using pre-processed EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran several classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% F1 score for 3-way classification). Our results, using EMG alone, are comparable to other researchers’, who used EMG and EEG together, in the literature. A running-window analysis further suggests that our method captures information in the EMG signal quickly and remains stable throughout the time that subjects grasp and move the object

Data Augmentation for Deep-Learning-Based Electroencephalography

Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

Data Augmentation for Deep-Learning-Based Electroencephalography

Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

An End-to-End CNN with Attentional Mechanism Applied to Raw EEG in a BCI Classification Task

Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based motor-imagery classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing. Approach. To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, Brain-Computer Interface (BCI) Competition IV 2a and 2b. BCI. Main results. Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning. Significance. Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI

Spinal Anesthesia Reduces Myocardial Ischemia-triggered Ventricular Arrhythmias by Suppressing Spinal Cord Neuronal Network Interactions in Pigs

Background: Cardiac sympathoexcitation leads to ventricular arrhythmias. Spinal anesthesia modulates sympathetic output and can be cardioprotective. However, its effect on the cardio-spinal reflexes and network interactions in the dorsal horn cardiac afferent neurons and the intermediolateral nucleus sympathetic neurons that regulate sympathetic output is not known. The authors hypothesize that spinal bupivacaine reduces cardiac neuronal firing and network interactions in the dorsal horn–dorsal horn and dorsal horn–intermediolateral nucleus that produce sympathoexcitation during myocardial ischemia, attenuating ventricular arrhythmogenesis. Methods: Extracellular neuronal signals from the dorsal horn and intermediolateral nucleus neurons were simultaneously recorded in Yorkshire pigs (n = 9) using a 64-channel high-density penetrating microarray electrode inserted at the T2 spinal cord. Dorsal horn and intermediolateral nucleus neural interactions and known markers of cardiac arrhythmogenesis were evaluated during myocardial ischemia and cardiac load–dependent perturbations with intrathecal bupivacaine. Results: Cardiac spinal neurons were identified based on their response to myocardial ischemia and cardiac load–dependent perturbations. Spinal bupivacaine did not change the basal activity of cardiac neurons in the dorsal horn or intermediolateral nucleus. After bupivacaine administration, the percentage of cardiac neurons that increased their activity in response to myocardial ischemia was decreased. Myocardial ischemia and cardiac load–dependent stress increased the short-term interactions between the dorsal horn and dorsal horn (324 to 931 correlated pairs out of 1,189 pairs, P \u3c 0.0001), and dorsal horn and intermediolateral nucleus neurons (11 to 69 correlated pairs out of 1,135 pairs, P \u3c 0.0001). Bupivacaine reduced this network response and augmentation in the interactions between dorsal horn–dorsal horn (931 to 38 correlated pairs out of 1,189 pairs, P \u3c 0.0001) and intermediolateral nucleus–dorsal horn neurons (69 to 1 correlated pairs out of 1,135 pairs, P \u3c 0.0001). Spinal bupivacaine reduced shortening of ventricular activation recovery interval and dispersion of repolarization, with decreased ventricular arrhythmogenesis during acute ischemia. Conclusions: Spinal anesthesia reduces network interactions between dorsal horn–dorsal horn and dorsal horn–intermediolateral nucleus cardiac neurons in the spinal cord during myocardial ischemia. Blocking short-term coordination between local afferent–efferent cardiac neurons in the spinal cord contributes to a decrease in cardiac sympathoexcitation and reduction of ventricular arrhythmogenesis

Machine-Learning-Based Approach to Decoding Physiological and Neural Signals

In recent years, machine learning algorithms have been developing rapidly, becoming increasingly powerful tools in decoding physiological and neural signals. The aim of this dissertation is to develop computational tools, and especially machine learning techniques, to identify the most effective methods for feature extraction and classification of these signals. This is particularly challenging due to the highly non-linear, non-stationery, and artifact- and noise-prone nature of these signals. Among basic human-control tasks, reaching and grasping are ubiquitous in everyday life. I investigated different linear and non-linear dimensionality reduction techniques for feature extraction and classification of electromyography (EMG) during a reach-grasp-lift task. The results highlighted the advantages of completely automated feature-learning by Laplacian Eigenmaps over manual feature engineering, especially when combined with classification, to achieve high accuracy with few training samples. The ability to decode and reduce the complexity of EMG could enable new practical applications for EMG in basic science and in the clinic. It could also help design humanoid and other robots. Beyond EMG, a key objective of my dissertation was to decode brain activity during the decision-making processes that lead to voluntary action. This was based on electroencephalography (EEG) and holds the promise for improved brain-computer interfaces (BCIs), particularly related to motor imagery (MI). We developed an end-to-end convolutional neural network with attentional mechanism together with different data augmentation techniques on two benchmark MI datasets. I also collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks. This enabled us to directly compare the decodability of MI and ME, investigate optimal channel configurations, and much more. In particular, this facilitates the analysis and decoding of MI on the fly—online and in in real time. Another potential use of EEG is measuring brain activity of people who are floating in body-temperature water in a sensory-deprivation-tank float pod. We compared lying in the float pod versus lying in bed (a control condition). And we found differences between the two, especially in the gamma band. More research is required to understand what these findings mean for levels of stress in the float pod

Electromyography Pattern Classification with Laplacian Eigenmaps in Human Running

Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research

Data Augmentation for Deep-Learning-Based Electroencephalography

Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8 % for recombination of segmentation and 36 % for noise addition and from 14 % for motor imagery to 56 % for mental workload—29 % on average. Conclusions DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis