EEG and EMG dataset for the detection of errors introduced by an active orthosis device.
Ontology highlight
ABSTRACT:
SUBMITTER: Kueper N
PROVIDER: S-EPMC10839100 | biostudies-literature | 2024
REPOSITORIES: biostudies-literature
Publications
EEG and EMG dataset for the detection of errors introduced by an active orthosis device.
Kueper Niklas N Chari Kartik K Bütefür Judith J Habenicht Julia J Rossol Tobias T Kim Su Kyoung SK Tabie Marc M Kirchner Frank F Kirchner Elsa Andrea EA
Frontiers in human neuroscience 20240122
Similar Datasets
Project description:Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.Supplementary informationThe online version contains supplementary material available at 10.1007/s11571-021-09776-w.
| S-EPMC9508316 | biostudies-literature
Project description:BackgroundSeizure detection is challenging outside the clinical environment due to the lack of comfortable, reliable, and practical long-term neurophysiological monitoring devices. We developed a novel, discreet, unobstructive in-ear sensing system that enables long-term electroencephalography (EEG) recording. This is the first study we are aware of that systematically compares the seizure detection utility of in-ear EEG with that of simultaneously recorded intracranial EEG. In addition, we present a similar comparison between simultaneously recorded in-ear EEG and scalp EEG.MethodsIn this foundational research, we conducted a clinical feasibility study and validated the ability of the ear-EEG system to capture focal-onset seizures against 1255 hrs of simultaneous ear-EEG data along with scalp or intracranial EEG in 20 patients with refractory focal epilepsy (11 with scalp EEG, 8 with intracranial EEG, and 1 with both).ResultsIn a blinded, independent review of the ear-EEG signals, two epileptologists were able to detect 86.4% of the seizures that were subsequently identified using the clinical gold standard EEG modalities, with a false detection rate of 0.1 per day, well below what has been reported for ambulatory monitoring. The few seizures not detected on the ear-EEG signals emanated from deep within the mesial temporal lobe or extra-temporally and remained very focal, without significant propagation. Following multiple sessions of recording for a median continuous wear time of 13 hrs, patients reported a high degree of tolerance for the device, with only minor adverse events reported by the scalp EEG cohort.ConclusionsThese preliminary results demonstrate the potential of using ear-EEG to enable routine collection of complementary, prolonged, and remote neurophysiological evidence, which may permit real-time detection of paroxysmal events such as seizures and epileptiform discharges. This study suggests that the ear-EEG device may assist clinicians in making an epilepsy diagnosis, assessing treatment efficacy, and optimizing medication titration.
| S-EPMC10848360 | biostudies-literature
Project description:Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman's correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system.
| S-EPMC6928739 | biostudies-literature
Project description:Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added as inputs to ICA in order to "force" the most power from EMG artifacts into a few independent components (ICs). The ICs containing EMG artifacts (the "artifact ICs") were identified and rejected using an automated procedure. ERASE was validated first using both simulated and experimentally-recorded EEG and EMG. Simulation results showed ERASE removed EMG artifacts from EEG significantly more effectively than conventional ICA. Also, it had a low false positive rate and high sensitivity. Subsequently, EEG was collected from 8 healthy participants while they moved their hands to test the realistic efficacy of this approach. Results showed that ERASE successfully removed EMG artifacts (on average, about 75% of EMG artifacts were removed when using real EMGs as reference artifacts) while preserving the expected EEG features related to movement. We also tested the ERASE procedure using simulated EMGs as reference artifacts (about 63% of EMG artifacts removed). Compared to conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG. These findings suggest that ERASE can achieve significant separation of EEG signal and EMG artifacts without a loss of the underlying EEG features. These results indicate that using additional real or simulated EMG sources can increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined with automated artifact IC rejection, ERASE also minimizes potential user bias. Future work will focus on improving ERASE so that it can also be used in real-time applications.
| S-EPMC7873899 | biostudies-literature
Project description:Using a novel wearable surface electromyography (sEMG), we investigated induced affective states by measuring the activation of facial muscles traditionally associated with positive (left/right orbicularis and left/right zygomaticus) and negative expressions (the corrugator muscle). In a sample of 38 participants that watched 25 affective videos in a virtual reality environment, we found that each of the three variables examined-subjective valence, subjective arousal, and objective valence measured via the validated video types (positive, neutral, and negative)-sEMG amplitude varied significantly depending on video content. sEMG aptitude from "positive muscles" increased when participants were exposed to positively valenced stimuli compared with stimuli that was negatively valenced. In contrast, activation of "negative muscles" was elevated following exposure to negatively valenced stimuli compared with positively valenced stimuli. High arousal videos increased muscle activations compared to low arousal videos in all the measured muscles except the corrugator muscle. In line with previous research, the relationship between sEMG amplitude as a function of subjective valence was V-shaped.
| S-EPMC9542454 | biostudies-literature
Project description:Aging disrupts circadian clocks, as evidenced by a reduction in the amplitude of circadian rhythms. Because the circadian clock strongly influences sleep-wake behavior in mammals, age-related alterations in sleep-wake patterns may be attributable, at least partly, to functional changes in the circadian clock. However, the effect of aging on the circadian characteristics of sleep architecture has not been well assessed, as circadian behaviors are usually evaluated through long-term behavioral recording with wheel-running or infrared sensors. In this study, we examined age-related changes in circadian sleep-wake behavior using circadian components extracted from electroencephalography (EEG) and electromyography (EMG) data. EEG and EMG were recorded from 12 to 17-week-old and 78 to 83-week-old mice for 3 days under light/dark and constant dark conditions. We analyzed time-dependent changes in the duration of sleep. Rapid eye movement (REM) and non-REM (NREM) sleep significantly increased during the night phase in old mice, whereas no significant change was observed during the light phase. The circadian components were then extracted from the EEG data for each sleep-wake stage, revealing that the circadian rhythm in the power of delta waves during NREM sleep was attenuated and delayed in old mice. Furthermore, we used machine learning to evaluate the phase of the circadian rhythm, with EEG data serving as the input and the phase of the sleep-wake rhythm (environmental time) as the output. The results indicated that the output time for the old mice data tended to be delayed, specifically at night. These results indicate that the aging process significantly impacts the circadian rhythm in the EEG power spectrum despite the circadian rhythm in the amounts of sleep and wake attenuated but still remaining in old mice. Moreover, EEG/EMG analysis is useful not only for evaluating sleep-wake stages but also for circadian rhythms in the brain.
| S-EPMC10213445 | biostudies-literature
Project description:Nowadays, surface electromyography (sEMG) is evolving as a technology for hand gesture recognition. Detailed studies have revealed the capacity of EMG signals to access detailed information, particularly in the classification of hand gestures. Indeed, this advancement emerges as an interesting element in refining the recognition and interpretation of sign languages and exploring deeper into the phonology of signed languages. Aligned with this advancement and the need for a reliable and mobile sign language recognition system, we introduce a specialized sEMG dataset, acquired using the Myo armband. This device is adept at capturing recordings at frequencies of up to 200 Hz. The dataset focuses on the 28 letters of the Arabic alphabet and 10 digits using hand gestures, with each gesture captured into 400 frames. This considerable collection of 18,716 samples was achieved with the cooperation of three contributors, providing a varied and comprehensive range of gestural data.
| S-EPMC10661691 | biostudies-literature
Project description:Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.
| S-EPMC9185290 | biostudies-literature
Project description:A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.
| S-EPMC5539567 | biostudies-literature
Project description:User behavior plays a substantial role in shaping household energy use. Nevertheless, the methodologies employed by researchers to examine user behavior exhibit certain limitations in terms of their reach. The present article introduces an openly accessible collection of electroencephalography (EEG) recordings, comprising EEG data collected from individuals who were subjected to energy data visualizations. The dataset comprises EEG recordings obtained from 28 individuals who were in good health. The EEG recordings were collected using a 32-channel EMOTIV EEG device, and the international 10-20 electrode system was employed for precise electrode placement. The energy data visualizations were generated and showcased utilizing the PsychoPy software. To ascertain the participants' affective state, they were requested to rate the valence and arousal of each stimulus through the utilization of a self-assessment manikin (SAM). Additionally, three inquiries were posed for every stimulation. The dataset includes both original data visualizations and ratings. Additionally, the raw EEG data has been divided into segments consisting of data visualizations and neutral images, with the use of event markers, in order to assist analysis. The EEG recordings were recorded and stored utilizing the EMOTIVPro application, whereas the subjective reactions were captured and preserved using the PsychoPy application. Furthermore, the generation of synthetic EEG data is accomplished by employing the Generative Adversarial Network (GAN) architecture on the acquired EEG dataset. The synthetic EEG data created is integrated with empirical EEG data, and afterwards subjected to qualitative and quantitative analysis in order to improve performance. The dataset presented herein showcases a pioneering utilization of EEG investigation and offers a valuable foundation for scholars in the domains of computer science, energy conservation, artificial intelligence, brain-computer interfaces, and human-computer interaction.
| S-EPMC10733112 | biostudies-literature