Decoding Movement From Electrocorticographic Activity: A Review

Volkova, Ksenia; Lebedev, Mikhail А.; Kaplan, Arline; Ossadtchi, Alexei

doi:10.3389/fninf.2019.00074

Cited by 82 publications

(54 citation statements)

References 199 publications

(391 reference statements)

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“…State-of-the-art EEG-based BMIs boosted by deep-learning methods have proven to be feasible for multidirectional 3D robotic arm control, with success rates of about 60% [132]. Even the more invasive methods, such as ECoG, hardly surpass the 80% success rate in 2D cursor control tasks [133]. Although these are impressive results with a wide range of practical applications, BCVs require stable, high success rates.…”

Section: A State-of-the-artmentioning

confidence: 99%

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Moioli

Nardelli

Barros

et al. 2021

IEEE Commun. Surv. Tutorials

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This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two wellestablished domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-ofthe-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and nextgeneration wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human-and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brainmachine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor.

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Section: A State-of-the-artmentioning

confidence: 99%

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Moioli

Nardelli

Barros

et al. 2021

IEEE Commun. Surv. Tutorials

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“…This electrode configuration is not optimised for LFP recordings as it has been shown that the properties and decoding performance of LFP signals depend on the recording electrode depth [41,49]. There is also increasing interest in using surface contact electrodes for BMI applications which offer adequate spatio-temporal resolution and less traumatic for the brain tissue [50,51]. Analyses on the impact of referencing scheme on the decoding performance from these types of electrodes are out of scope of our present study and can be potential direction for future study.…”

Section: Discussionmentioning

confidence: 99%

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

2021

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Objective. There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs. Approach. To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks. Main results. Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance. Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.

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“…Additionally, the incidence rate of of beta bursts in the primary somatosensory cortex is inversely correlated with the ability to detect tactile stimuli [24] and also affects other motor functions. Intracranial recordings, such as ECoG, allow reliable measurement of the faster gamma band activity, which is temporally and spatially specific to movement patterns [26] and is thought to accompany movement control and execution. Overall, based on the very solid body of research, rhythmic components of brain sources, s(t), appear to be useful for BCI implementations.Given the linearity of the generative model ( 1), these rhythmic signals reflecting the activity of specific neuronal populations can be computed as linear combinations of narrow-band filtered sensor data x(t).…”

Section: Methodsmentioning

confidence: 99%

Decoding neural signals and discovering their representations with a compact and interpretable convolutional neural network

Petrosuan¹,

Lebedev

Ossadtchi³

2020

Preprint

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Brain-computer interfaces (BCIs) decode information from neural activity and send it to external 1 devices. In recent years, we have seen an emergence of new algorithms for BCI decoding. Here 2 we propose a compact architecture for adaptive decoding of electrocorticographic (ECoG) data into 3 finger kinematics. We also describe a theoretically justified approach to interpreting the spatial 4 and temporal weights in the architectures that combine adaptation in both space and time, such as 5 ours. In hese architectures the weights are optimized not only for decoding of target signals but 6 also for tuning away from the interfering sources, in both the spatial and the frequency domains. 7 When applied to a dataset taken from the repository of Berlin BCI IV competition, our architecture 8 outperformed the competition winners without the need for feature selection. Moreover, by looking 9 at the architecture weights we could explain in physiological terms how our algorithm decodes 10 spatial and temporal parameters of finger kinematics. As such, the proposed architecture offers a 11 good decoder and a tool for investigating neural mechanisms of motor control. 15Brain-computer interfaces (BCIs) link the nervous system to external devices [4] or even the other brains [15]. While 16 there exist many applications of BCIs [1], clinically relevant BCIs have received most attention that aid in rehabilitation 17 of patients with sensory, motor, and cognitive disabilities [12]. Clinical uses of BCIs range from assistive devices to 18 neural prostheses that restore functions abolished by neural trauma or disease [2]. 19 BCIs can deal with a variety of neural signals [14, 8] such as, for example, electroencephalographic (EEG) potentials 20 sampled with electrodes placed on the surface of the head [11], or neural activity recorded invasively with the elec-21 trodes implanted in the cortex [6] or places onto the cortical surface [18]. The latter method, which we consider here, 22is called electrocorticography (ECoG ). Accurate decoding of neural signals is key to building efficient BCIs. 23A PREPRINT -JUNE 2, 2020 BCI signal processing comprises several steps, including signal conditioning, feature extraction, and decoding. In 24 the modern machine-learning algorithms, feature extraction and decoding are not separate but rather simultaneous 25 computations performed with the computational architectures called Deep Neural Networks (DNN) [9]. DNNs de-26 rive features automatically when executing regression or classification tasks. While it is often difficult to interpret 27 the computations performed by a DNN, such interpretations are essential to gain understanding of the properties of 28 brain activity contributing to decoding, and to ensure that artifacts do not affect the decoding results. In particular, 29 interpretation of features computed by the first several layers of a DNN could shed light on the neurophysiological 30 mechanisms underlying the behavior being studied. Ideally, by examining DNN weights, one should be able...

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Decoding Movement From Electrocorticographic Activity: A Review

Cited by 82 publications

References 199 publications

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Impact of referencing scheme on decoding performance of LFP-based brain-machine interface

Decoding neural signals and discovering their representations with a compact and interpretable convolutional neural network

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