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1 e potential application of LFP signals for a brain-machine interface.
2 es is the key to extending the impact of the brain-machine interface.
3 bjective states of actions generated via the brain-machine interface.
4 heir hand or directly from the brain using a brain-machine interface.
5 ent learning principles toward an autonomous brain-machine interface.
6 sed to control external devices as part of a brain-machine interface.
7 t it can be successfully incorporated into a brain-machine interface.
8 ly expands the source of control signals for brain-machine interfaces.
9 l and useful addition to therapeutic uses of brain-machine interfaces.
10 ld enhance the performance and robustness of brain-machine interfaces.
11 otential applications to active implants for brain-machine interfaces.
12 transform clinical mapping and research with brain-machine interfaces.
13 ngs and are extensively used for research in brain-machine interfaces.
14 c spatiotemporal characteristics in refining brain-machine interfaces.
15 isease states, and as a potential signal for brain-machine interfaces.
16 mportant implications for the development of brain-machine interfaces.
17 ntal challenge in developing next-generation brain-machine interfaces.
18 rn are relevant for clinical applications of brain-machine interfaces.
19 ce of bio-matter, bio-chemical sciences, and brain-machine interfaces.
20 , and for the development of next-generation brain-machine interfaces.
21 ng sense of agency in nonnatural cases, like brain-machine interfaces.
22 ceptive feedback in clinical applications of brain-machine interfaces.
23 pacts future applications in devices such as brain-machine interfaces.
24 osing and treating disease and for improving brain/machine interfaces.
25 tentiate cognitive processes or behavior via brain-machine interfacing.
26   Single-trial decoding is a prerequisite to brain-machine interfaces, a key application that could b
27 in the MRP corroborating its suitability for brain-machine interfaces, although information about gra
28 rtical states in freely behaving animals for brain-machine interface and delivered electrochemical sp
29  been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation th
30 ile, Santiago, Chile; and the Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia
31 ving the way for more adaptive and efficient brain-machine interfaces and neuroprosthetics.
32 urons, making them interesting for conformal brain-machine interfaces and other wearable bioelectroni
33 ented this method in a real-time biofeedback brain-machine interface, and found that monkeys could le
34 ental for constructing computational models, brain-machine interfaces, and neuroprosthetics.
35 rning applications, such as computer vision, brain-machine interfaces, and precision care.
36 probes have led to advances in neuroscience, brain-machine interfaces, and treatment of neurological
37   These results have strong implications for Brain Machine Interface applications and for study of po
38  in addition to force control in restorative brain-machine interface applications.
39 ol for clinical, cognitive neuroscience, and brain-machine-interfacing applications.
40                                              Brain-machine interfaces are being developed to assist p
41                                              Brain-machine interfaces are not only promising for neur
42  with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control
43 ens the door for the direct wiring of robust brain-machine interfaces as well as for investigations o
44     This result is particularly important to brain-machine interfaces because it could enable stable
45 l M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements.
46 ships are learned and effected, we devised a brain machine interface (BMI) task using wide-field calc
47 tions and actions in a tetraplegic user of a brain machine interface (BMI), decoding primary motor co
48 col that integrates locomotion training with brain machine interfaces (BMI).
49 This technology is commonly referred to as a Brain-Machine Interface (BMI) and is achieved by predict
50 d their stability can shed light on improved brain-machine interface (BMI) approaches to decode these
51                                Here we use a brain-machine interface (BMI) based on real-time magneto
52             Here, we show that a closed-loop brain-machine interface (BMI) can modulate sensory-affec
53               Here, we designed and tested a brain-machine interface (BMI) combining an automated pai
54                        To evaluate whether a brain-machine interface (BMI) could be used to regain co
55 vements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of
56 m cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could
57 Studies on neural plasticity associated with brain-machine interface (BMI) exposure have primarily do
58 n outfitted with a primary motor cortex (M1) brain-machine interface (BMI) generating real hand movem
59                         Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys
60 ning and its limits in a human intracortical brain-machine interface (BMI) paradigm.
61                       This adversely impacts brain-machine interface (BMI) performance for patients w
62 roprosthetic system is also referred to as a brain-machine interface (BMI) since it interfaces the br
63                      The results from recent brain-machine interface (BMI) studies suggest that it ma
64 lar cortex as rats learned a neuroprosthetic/brain-machine interface (BMI) task.
65 ectrode array to rat's gustatory cortex with brain-machine interface (BMI) technology.
66  this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping p
67 re rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and
68                                      Using a brain-machine interface (BMI) that transforms rhesus mac
69         Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the h
70 as the brain-computer interface (BCI) or the brain-machine interface (BMI), are gaining momentum in t
71 de interest in the possibility of creating a brain-machine interface (BMI), particularly as a means t
72 potential targets for less invasive forms of brain-machine interface (BMI).
73  decoding of cortical motor commands using a brain-machine interface (BMI).
74 ivity, we incorporate neural dynamics into a brain-machine interface (BMI).
75                                              Brain-machine interfaces (BMI) create novel sensorimotor
76 ation of brain-computer interfaces (BCI) and brain-machine interfaces (BMI) depends significantly on
77 ficant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstra
78               Much progress has been made in brain-machine interfaces (BMI) using decoders such as Ka
79 f the successful strategies for implementing brain-machine interfaces (BMI), by which the subject lea
80 tracortical microelectrodes (IMEs) used with brain-machine interfacing (BMI) applications is regarded
81                                        Motor brain machine interfaces (BMIs) directly link the brain
82                                         Such brain machine interfaces (BMIs) have allowed animal subj
83                                              Brain machine interfaces (BMIs) hold promise to restore
84                                              Brain-machine interfaces (BMIs) aim to help people with
85                                Intracortical brain-machine interfaces (BMIs) aim to restore lost moto
86 an be used to predict reach intentions using brain-machine interfaces (BMIs) and therefore assist tet
87                                              Brain-machine interfaces (BMIs) are powerful tools to st
88 responses are relevant to the development of brain-machine interfaces (BMIs) because they provide a r
89                                              Brain-machine interfaces (BMIs) create closed-loop contr
90                                              Brain-machine interfaces (BMIs) enable people living wit
91                               Traditionally, brain-machine interfaces (BMIs) extract motor commands f
92                                              Brain-machine interfaces (BMIs) for reaching have enjoye
93  A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor
94 o control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impre
95                                     Although brain-machine interfaces (BMIs) have focused largely on
96    A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders
97                        The field of invasive brain-machine interfaces (BMIs) is typically associated
98                                Intracortical brain-machine interfaces (BMIs) may eventually restore f
99                                              Brain-machine interfaces (BMIs) offer a powerful tool to
100                                              Brain-machine interfaces (BMIs) offer the promise of rec
101                                              Brain-machine interfaces (BMIs) provide a framework for
102                                              Brain-machine interfaces (BMIs) provide a framework for
103                                              Brain-machine interfaces (BMIs) provide a new assistive
104                                              Brain-machine interfaces (BMIs) should ideally show robu
105        Nicolelis wrote in his 2003 review on brain-machine interfaces (BMIs) that the design of a suc
106     It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assum
107                                       Speech brain-machine interfaces (BMIs) translate brain signals
108 his network are attractive implant sites for brain-machine interfaces (BMIs).
109 ions of this neural population structure for brain-machine interfaces (BMIs).
110 ecessary for successful motor prostheses and brain-machine interfaces (BMIs).
111 siological principles and the development of brain-machine interfaces (BMIs).
112 omagnetic noise, an interesting modality for brain-machine interfaces (BMIs).
113 MC) or under direct neural control through a brain-machine interface (Brain Control, BC).
114  recording electrodes are regularly used for Brain Machine Interfaces, but the information content va
115 is question, we used a calcium-imaging-based brain-machine interface (CaBMI)(3) and trained different
116                                              Brain-machine interfaces can allow neural control over a
117 priate neural state, prosthetic implants and brain-machine interfaces can be designed based on these
118                             It is hoped that brain-machine interfaces can be used to restore the norm
119                                     Invasive brain-machine interfaces can restore motor, sensory and
120                                       Modern brain-machine interfaces can return function to people w
121 e human posterior parietal cortex (PPC) of a brain-machine interface clinical trial participant impla
122 ignals, supported high performance real-time brain-machine interface control.
123                                              Brain-machine interfaces could provide a solution to res
124                                  Traditional brain-machine interfaces decode cortical motor commands
125                                Intracortical brain-machine interfaces decode motor commands from neur
126 s of performance.SIGNIFICANCE STATEMENT Many brain-machine interface decoders have been constructed f
127 real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 1
128                                            A brain-machine interface demonstrates volitional control
129                         The success of these brain-machine interfaces depends on the electrodes that
130  cortical control of movement as well as for brain-machine interface development.
131 new generation of diagnostic and therapeutic brain-machine interface devices.
132 o facilitate the development of long-lasting brain-machine interface devices.
133                               We developed a brain-machine interface for aDBS, which enables modulati
134 se confirmed that our probes can form stable brain-machine interfaces for at least 2 months.
135  fundamental challenge in the development of brain-machine interfaces for neurological treatments.
136 s and could be applied in the development of brain-machine interfaces for restoring speech in paralys
137 n of cortical circuits and bears promise for brain-machine interfaces for sensory and motor function
138 s via an approach less invasive than current brain-machine interfaces for visual restoration.
139 or restoring the hand to cortex pathway with brain-machine interfaces, for bionic prosthetics, or bio
140                                              Brain-machine interfaces have great potential for the de
141 e cognition, which are relevant for advanced brain-machine interfaces, improved therapies for neurolo
142 , we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utiliz
143 ystematic comparison of their efficiency for Brain Machine Interfaces is important but technically ch
144                                  One type of brain-machine interface is a cortical motor prosthetic,
145       The large power requirement of current brain-machine interfaces is a major hindrance to their c
146 ating assisted locomotion with a noninvasive brain-machine interface (L + BMI), virtual reality, and
147  applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic com
148                   Innovation in the field of brain-machine interfacing offers a new approach to manag
149                      Here, we used a sensory brain-machine interface paradigm, permitting both free b
150  population activities as required, e.g., in brain-machine interface paradigms.
151 y are compatible with the mandatory needs of brain-machine interfaces, particularly for visual restor
152                                              Brain-machine interface performance can be affected by n
153  spiking activity could control a biomimetic brain-machine interface reflecting ipsilateral kinematic
154 current circumstances.SIGNIFICANCE STATEMENT Brain-machine interfaces represent a solution for physic
155 ics, suggesting that accurate operation of a brain-machine interface requires recording from large ne
156 mportant implications for the development of brain-machine interfaces.SIGNIFICANCE STATEMENT Locomoti
157 eriments, the neural control engineering and brain-machine interface studies.
158 nd is of growing importance in cognitive and brain-machine-interfacing studies.
159  control, it may serve in the development of brain machine interfaces that also use ipsilateral neura
160            Here we demonstrate a closed loop brain-machine interface that delivers electrical stimula
161 of an integrated nanomedicine-bioelectronics brain-machine interface that enables continuous and on-d
162                               We developed a brain-machine interface that initiated task trials based
163  Zhang and colleagues designed a closed-loop brain-machine interface that learned to reduce participa
164 ased fibre probe tested in vivo for a stable brain-machine interface that paves the way towards innov
165  probabilistic population coding and lead to brain-machine interfaces that more accurately reflect co
166   Despite the rapid progress and interest in brain-machine interfaces that restore motor function, th
167 unctional skin-like electronics, and improve brain/machine interfaces that enable transmission of the
168 ted with actions generated via intracortical brain-machine interfaces, the neural mechanisms involved
169                               We developed a brain-machine interface to test whether rats can do so b
170 t that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tas
171 litate decoding of brain activity when using brain-machine interfaces to overcome loss of function af
172  patterns have been used in the new field of brain-machine interfaces to show how cursors on computer
173 trolled first via a joystick and later via a brain-machine interface-to find the object with denser v
174                                              Brain-machine-interface training was done for 13 weeks w
175 se of agency affected the proficiency of the brain-machine interface, underlining the clinical potent
176                                              Brain-machine interfaces use neuronal activity recorded
177 ng how subjects learned de novo to control a brain-machine interface using neurons from motor cortex.
178 feedback in a tetraplegic individual using a brain-machine interface, we provide evidence that primar
179  utility of this internal model estimate for brain-machine interfaces, we performed an offline analys
180 t and behavior and have been used to control Brain Machine Interfaces with varying degrees of success
181                               The autonomous brain-machine interface would use M1 for both decoding m

 
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