Remapping cortical modulation for electrocorticographic brain–computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis

Degenhart, Alan D.; Hiremath, Shivayogi V.; Yang, Ying; Foldes, Stephen T.; Collinger, Jennifer L.; Boninger, Michael L.; Tyler-Kabara, Elizabeth C.; Wang, Wei

doi:10.1088/1741-2552/aa9bfb

Cited by 42 publications

(45 citation statements)

References 88 publications

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“…For example, a recent study using ECoG in humans has shown that S1 activates before M1 during hand movement (Sun et al , 2015). In addition, high accuracy decoding of hand and finger movements from S1 has been demonstrated by recent BCI studies performed with paralyzed patients (Wang et al , 2013; Degenhart et al , 2018) and people with arm amputation (Kikkert et al , 2016; Bruurmijn et al , 2017). These results support data from earlier fMRI and electrocortical stimulation studies, in particular the findings 1) in individuals with spinal cord injury, who show fMRI activation of S1 during attempted movements (Cramer et al , 2005; Hotz-boendermaker et al , 2008, 2011); 2) in individuals with induced ischemic nerve blocking, who had preserved fMRI responses without sensory feedback (Christensen et al , 2007); and 3) in individuals with epilepsy who show isolated and complex hand responses upon cortical stimulation of S1 (Nii et al , 1996; Haseeb et al , 2007).…”

Section: Discussionmentioning

confidence: 89%

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain‐Computer Interface perspective

Branco

Boer

Ramsey

et al. 2019

Eur J of Neuroscience

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For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

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Section: Discussionmentioning

confidence: 89%

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain‐Computer Interface perspective

Branco

Boer

Ramsey

et al. 2019

Eur J of Neuroscience

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“…In other words, BCI systems can provide alternative communication channels for LIS patients [ 3 ]. BCIs can be realized using various neuroimaging modalities, such as electrocorticography (ECoG) [ 4 , 5 ], electroencephalography (EEG) [ 6 , 7 ], magnetoencephalography (MEG) [ 8 , 9 ], functional near-infrared spectroscopy (fNIRS) [ 10 , 11 ], and functional magnetic resonance imaging (fMRI) [ 12 , 13 ]. An invasive signal recording technique, such as ECoG, requires a surgical operation to place recording electrodes on the cortex.…”

Section: Introductionmentioning

confidence: 99%

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Choi

Han

Choi

et al. 2018

Sensors

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Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

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“…Biomimetic decoding exploits the mapping which relates neuronal activity to limb movement before the patient began to suffer from motor disabilities; that is, it uses the activity of neurons naturally devoted to the control of a specific limb to compute the commands sent to the corresponding prothesis or orthosis. These decoders are often referred to as “direct” decoders [“direct motor Brain Machine Interfaces” (Waldert et al, 2009 ), “direct mapping” (Degenhart et al, 2018 )]. Most biomimetic decoders are kinematic.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the brain patterns that are used to control the prosthesis or orthosis movements are elicited by mental tasks, such as motor imageries (somatotopic remapping), and cognitive tasks. “Mental-task” decoding (Waldert et al, 2009 ) or “abstract” mapping (Degenhart et al, 2018 ) are some of the terms that have been used in the literature to refer to BCIs based on unnatural motor imageries and cognitive tasks or strategies. Various mental tasks have been used to elicit intention-specific and distinguishable brain patterns for neural control in motor BCI systems (Waldert et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

“…Mental-task decoders have also been used for cursor or prosthesis control from ECoG signals (Wang W. et al, 2013 ) and, occasionally, from SUA/MUA signals (Hochberg et al, 2006 ). More specifically, most online ECoG-driven motor BCI studies have been completed with mental-task decoders (Leuthardt et al, 2004 , 2006a , 2011 ; Schalk et al, 2008 ; Vansteensel et al, 2010 ; Degenhart et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Schaeffer

Aksenova

2018

Front. Neurosci.

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Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.

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Remapping cortical modulation for electrocorticographic brain–computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis

Abstract: These results demonstrate the feasibility of ECoG-based BCI systems for individuals with paralysis as well as highlight some of the key challenges that must be overcome before such systems are translated to the clinical realm. ClinicalTrials.gov Identifier: NCT01393444.

Cited by 42 publications

References 88 publications

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain‐Computer Interface perspective

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain‐Computer Interface perspective

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

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