A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements

Kilicarslan, Atilla; Grossman, Robert G.; Contreras-Vidal, José L.

doi:10.1088/1741-2560/13/2/026013

Cited by 132 publications

(109 citation statements)

References 20 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…This filter significantly increased decoding accuracy of user intentions using EEG to control a lower-body exoskeleton, as it was shown in (Kilicarslan et al, 2016). A previous study showed that quality of EEG signals (i.e., signal to noise ratio, standard deviation) affect decoding accuracies of treadmill walking (Luu et al, 2015), which was the case in this research.…”

Section: Discussionmentioning

confidence: 69%

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar

Luu

Brown

et al. 2016

J. Neural Eng.

View full text Add to dashboard Cite

Objective The control of human bipedal locomotion is of great interest to the field of lower-body brain computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences. Approach In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1 – 3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated (AM) potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Main results Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson’s r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8. Significance These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.

show abstract

Section: Discussionmentioning

confidence: 69%

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar

Luu

Brown

et al. 2016

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…In the case of a disagreement, a third reviewer (TPL) was decisive. Two additional publications that do not contain all the keywords in their titles were manually identified as relevant studies (Kilicarslan et al 2016.…”

Section: Search Methods For Identification Of Studiesmentioning

confidence: 99%

“…With little risk of falls, Lokomat provides a safe testing environment. The Rex exoskeleton keeps its balance without the support of crutches or any other external system, although in practice, human spotters are often required to guarantee safety (Kwak et al 2015, Kilicarslan et al 2016. The customized exoskeleton used in Donati et al (2016) is also reported to be stable in single support stance without the need of crutches as well.…”

Section: User and Robot Typesmentioning

confidence: 99%

“…Based on the continuity of states being decoded, they can be grouped into two categories: (1) Those featuring continuous reconstruction of trajectories, such as linear regression (Vouga et al 2017) (Kilicarslan et al 2013(Kilicarslan et al , 2016, random forest , multiple kernel learning (Zhang et al 2017), and maximizing canonical correlation coefficients (Kwak et al 2015).…”

Section: Decodersmentioning

confidence: 99%

See 1 more Smart Citation

Brain–machine interfaces for controlling lower-limb powered robotic systems

et al. 2018

Self Cite

View full text Add to dashboard Cite

Objective. Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain–machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking. Approach. To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared. Main results. Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others. Significance. We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.

show abstract

“…Therefore, most components of electrooculogram (EOG) as well as the low-frequency drifts and bias exerted few effects on this study [23, 24]. In addition, for artifacts with a frequency higher than 4Hz, we applied a statistic regarding its duration percentage to the series duration.…”

Section: Methodsmentioning

confidence: 99%

Brain Connectivity Variation Topography Associated with Working Memory

Huang

et al. 2016

PLoS ONE

View full text Add to dashboard Cite

Brain connectivity analysis plays an essential role in the research of working memory that involves complex coordination of various brain regions. In this research, we present a comprehensive view of trans-states brain connectivity variation based on continuous scalp EEG, extending beyond traditional stimuli-lock averaging or restriction to short time scales of hundreds of milliseconds after stimulus onset. The scalp EEG was collected under three conditions: quiet, memory, and control. The only difference between the memory and control conditions was that in the memory condition, subjects made an effort to retain information. We started our investigation with calibrations of Pearson correlation in EEG analysis and then derived two indices, link strength and node connectivity, to make comparisons between different states. Finally, we constructed and studied trans-state brain connectivity variation topography. Comparing memory and control states with quiet state, we found that the beta topography highlights links between T5/T6 and O1/O2, which represents the visual ventral stream, and the gamma topography conveys strengthening of inter-hemisphere links and weakening of intra-hemisphere frontal-posterior links, implying parallel inter-hemisphere coordination combined with sequential intra-hemisphere coordination when subjects are confronted with visual stimuli and a motor task. For comparison between memory and control states, we also found that the node connectivity of T6 stands out in gamma topography, which provides strong proof from scalp EEG for the information binding or relational processing function of the temporal lobe in memory formation. To our knowledge, this is the first time for any method to effectively capture brain connectivity variation associated with working memory from a relatively large scale both in time (from a second to a minute) and in space (from the scalp). The method can track brain activity continuously with minimal manual interruptions; therefore, it has promising potential in applications such as brain computer interfaces and brain training.

show abstract

A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements

Cited by 132 publications

References 20 publications

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar

Brain–machine interfaces for controlling lower-limb powered robotic systems

Brain Connectivity Variation Topography Associated with Working Memory

Contact Info

Product

Resources

About