scholarly journals Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces

2009 ◽  
Vol 27 (1) ◽  
pp. E10 ◽  
Author(s):  
Eric C. Leuthardt ◽  
Zac Freudenberg ◽  
David Bundy ◽  
Jarod Roland
Keyword(s):  
Motor Cortex ◽  
Minimally Invasive ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Future Application ◽  
Electrode Spacing ◽  
Sufficient Information ◽  
Invasive Monitoring ◽  
Seizure Localization

Object There is a growing interest in the use of recording from the surface of the brain, known as electrocorticography (ECoG), as a practical signal platform for brain-computer interface application. The signal has a combination of high signal quality and long-term stability that may be the ideal intermediate modality for future application. The research paradigm for studying ECoG signals uses patients requiring invasive monitoring for seizure localization. The implanted arrays span cortex areas on the order of centimeters. Currently, it is unknown what level of motor information can be discerned from small regions of human cortex with microscale ECoG recording. Methods In this study, a patient requiring invasive monitoring for seizure localization underwent concurrent implantation with a 16-microwire array (1-mm electrode spacing) placed over primary motor cortex. Microscale activity was recorded while the patient performed simple contra- and ipsilateral wrist movements that were monitored in parallel with electromyography. Using various statistical methods, linear and nonlinear relationships between these microcortical changes and recorded electromyography activity were defined. Results Small regions of primary motor cortex (< 5 mm) carry sufficient information to separate multiple aspects of motor movements (that is, wrist flexion/extension and ipsilateral/contralateral movements). Conclusions These findings support the conclusion that small regions of cortex investigated by ECoG recording may provide sufficient information about motor intentions to support brain-computer interface operations in the future. Given the small scale of the cortical region required, the requisite implanted array would be minimally invasive in terms of surgical placement of the electrode array.

scholarly journals Primary motor cortex has independent representations for ipsilateral and contralateral arm movements but correlated representations for grasping

2019 ◽  
Author(s):  
John E Downey ◽  
Kristin M Quick ◽  
Nathaniel Schwed ◽  
Jeffrey M Weiss ◽  
George F Wittenberg ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Human Subjects ◽  
Brain Computer Interface ◽  
Hand Movement ◽  
Arm Movement ◽  
Computer Interface ◽  
Contralateral Movement ◽  
Contralateral Motor ◽  
Continuous Movements

AbstractMotor commands for the arms and hands generally originate in contralateral motor cortex anatomically. However, ipsilateral primary motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we present the results of bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in left primary motor cortex for a clinical brain-computer interface study. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects’ ability to control both arms through a brain-computer interface system. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected, and poses new questions about the different ways primary motor cortex controls hands and arms.

scholarly journals Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons

2014 ◽  
Vol 112 (6) ◽  
pp. 1528-1548 ◽  
Author(s):  
Andrew J. Law ◽  
Gil Rivlis ◽  
Marc H. Schieber
Keyword(s):  
Motor Cortex ◽  
Degrees Of Freedom ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Rapid Acquisition ◽  
Interface Control ◽  
Large Ensembles ◽  
Performance Patterns ◽  
The Individual

Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture—such as the similarity of preferred directions—had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill.

scholarly journals Augmenting Quadriplegic Hand Function Using a Sensorimotor Demultiplexing Neural Interface

2019 ◽  
Author(s):  
PD Ganzer ◽  
SC Colachis ◽  
MA Schwemmer ◽  
DA Friedenberg ◽  
CE Swiftney ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Sensory Feedback ◽  
Closed Loop ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Sensory Information ◽  
Feedback System ◽  
Computer Interface ◽  
Sense Of Touch ◽  
The Brain

AbstractBackgroundThe sense of touch is a key component of motor function. Severe spinal cord injury (SCI) should essentially eliminate sensory information transmission to the brain, that originates from skin innervated from below the lesion. We assessed the hypothesis that, following SCI, residual hand sensory information is transmitted to the brain, can be decoded amongst competing sensorimotor signals, and used to enhance the sense of touch via an intracortically controlled closed-loop brain-computer interface (BCI) system.MethodsExperiments were performed with a participant who has an AIS-A C5 SCI and an intracortical recording array implanted in left primary motor cortex (M1). Sensory stimulation and standard clinical sensorimotor functional assessments were used throughout a series of several mechanistic experiments.FindingsOur results demonstrate that residual afferent hand sensory signals surprisingly reach human primary motor cortex and can be simultaneously demultiplexed from ongoing efferent motor intention, enabling closed-loop sensory feedback during brain-computer interface (BCI) operation. The closed-loop sensory feedback system was able to detect residual sensory signals from up to the C8 spinal level. Using the closed-loop sensory feedback system enabled significantly enhanced object touch detection, sense of agency, movement speed, and other sensorimotor functions.InterpretationTo our knowledge, this is the first demonstration of simultaneously decoding multiplexed afferent and efferent activity from human cortex to control multiple assistive devices, constituting a ‘sensorimotor demultiplexing’ BCI. Overall, our results support the hypothesis that sub-perceptual neural signals can be decoded reliably and transformed to conscious perception, significantly augmenting function.FundingInternal funding was provided for this study from Battelle Memorial Institute and The Ohio State University Center for Neuromodulation.

scholarly journals 2190 Longitudinal changes in EEG power envelope connectivity are proportional to motor recovery in chronic stroke patients

2018 ◽  
Vol 2 (S1) ◽  
pp. 17-17
Author(s):  
Joseph B. Humphries ◽  
David T. Bundy ◽  
Eric C. Leuthardt ◽  
Thy N. Huskey
Keyword(s):  
Motor Cortex ◽  
Motor Imagery ◽  
Stroke Rehabilitation ◽  
Brain Computer Interface ◽  
Chronic Stroke ◽  
Motor Recovery ◽  
Stroke Recovery ◽  
Computer Interface ◽  
Beta Band ◽  
Stroke Patients

OBJECTIVES/SPECIFIC AIMS: The objective of this study is to determine the degree to which the use of a contralesionally-controlled brain-computer interface for stroke rehabilitation drives change in interhemispheric motor cortical activity. METHODS/STUDY POPULATION: Ten chronic stroke patients were trained in the use of a brain-computer interface device for stroke recovery. Patients perform motor imagery to control the opening and closing of a motorized hand orthosis. This device was sent home with patients for 12 weeks, and patients were asked to use the device 1 hour per day, 5 days per week. The Action Research Arm Test (ARAT) was performed at 2-week intervals to assess motor function improvement. Before the active motor imagery task, patients were asked to quietly rest for 90 seconds before the task to calibrate recording equipment. EEG signals were acquired from 2 electrodes—one each centered over left and right primary motor cortex. Signals were preprocessed with a 60 Hz notch filter for environmental noise and referenced to the common average. Power envelopes for 1 Hz frequency bands (1–30 Hz) were calculated through Gabor wavelet convolution. Correlations between electrodes were then calculated for each frequency envelope on the first and last 5 runs, thus generating one correlation value per subject, per run. The chosen runs approximately correspond to the first and last week of device usage. These correlations were Fisher Z-transformed for comparison. The first and last 5 run correlations were averaged separately to estimate baseline and final correlation values. A difference was then calculated between these averages to determine correlation change for each frequency. The relationship between beta-band correlation changes (13–30 Hz) and the change in ARAT score was determined by calculating a Pearson correlation. RESULTS/ANTICIPATED RESULTS: Beta-band inter-electrode correlations tended to decrease more in patients achieving greater motor recovery (Pearson’s r=−0.68, p=0.031). A similar but less dramatic effect was observed with alpha-band (8–12 Hz) correlation changes (Pearson’s r=−0.42, p=0.22). DISCUSSION/SIGNIFICANCE OF IMPACT: The negative correlation between inter-electrode power envelope correlations in the beta frequency band and motor recovery indicates that activity in the motor cortex on each hemisphere may become more independent during recovery. The role of the unaffected hemisphere in stroke recovery is currently under debate; there is conflicting evidence regarding whether it supports or inhibits the lesioned hemisphere. These findings may support the notion of interhemispheric inhibition, as we observe less in common between activity in the 2 hemispheres in patients successfully achieving recovery. Future neuroimaging studies with greater spatial resolution than available with EEG will shed further light on changes in interhemispheric communication that occur during stroke rehabilitation.

scholarly journals Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience

2020 ◽  
pp. neurintsurg-2020-016862
Author(s):  
Thomas J Oxley ◽  
Peter E Yoo ◽  
Gil S Rind ◽  
Stephen M Ronayne ◽  
C M Sarah Lee ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Minimally Invasive ◽  
Activities Of Daily Living ◽  
Text Messaging ◽  
Daily Living ◽  
Primary Motor Cortex ◽  
Functional Independence ◽  
Human Experience ◽  
Digital Devices ◽  
Selection Accuracy

BackgroundImplantable brain–computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.MethodsTwo participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.ResultsUnsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%–100.00%) (trial mean (median, Q1–Q3)) at a rate of 13.81 (13.44, 10.96–16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%–100.00%) at 20.10 (17.73, 12.27–26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.ConclusionWe describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.

scholarly journals Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects

2013 ◽  
Vol 7 ◽  
Author(s):  
Olesya A. Mokienko ◽  
Alexander V. Chervyakov ◽  
Sofia N. Kulikova ◽  
Pavel D. Bobrov ◽  
Liudmila A. Chernikova ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Trained Subjects ◽  
Motor Cortex Excitability

scholarly journals Identifying and Decoding Temporally Correlated Neural Patterns in the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand Using Stereoelectroencephalography

2021 ◽  
Author(s):  
Chad Bouton ◽  
Nikunj Bhagat ◽  
Santosh Chandrasekaran ◽  
Jose Herrero ◽  
Noah Markowitz ◽  
...  
Keyword(s):  
Feature Selection ◽  
White Matter ◽  
Minimally Invasive ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Finger Movements ◽  
Temporal Autocorrelation ◽  
Individual Finger ◽  
Tactile Stimuli ◽  
The Brain

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore function in patients living with debilitating conditions. One of the challenges currently facing BCI technology, however, is how to minimize surgical risk. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications since they can lead to fewer complications. SEEG electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. We therefore investigated the viability of using SEEG electrodes in a BCI for recording and decoding neural signals related to movement and the sense of touch and compared its performance to electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns were variable trial-to-trial and transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on temporal autocorrelation, a repeatability metric. An algorithm based on temporal autocorrelation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling both transient and sustained input features. Combining temporal autocorrelation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 +/- 1.51% for hand movements, up to 91.69 +/- 0.49% for individual finger movements, and up to 80.64 +/- 1.64% for focal tactile stimuli to the finger pads and palm while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wider variety of conditions.

Pleasure detection from the facial motor cortex by the brain-computer interface

2009 ◽  
Author(s):  
Zia Mohy Ud-Din ◽  
Sang Hyo Woo ◽  
Wei Qun ◽  
Jee Hyun Kim ◽  
Hwan Soo Jang ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
The Brain
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