ライフサイエンスコーパス: brain-machine interface

1 heir hand or directly from the brain using a brain-machine interface.

2 ent learning principles toward an autonomous brain-machine interface.

3 sed to control external devices as part of a brain-machine interface.

4 t it can be successfully incorporated into a brain-machine interface.

5 e potential application of LFP signals for a brain-machine interface.

6 es is the key to extending the impact of the brain-machine interface.

7 ng sense of agency in nonnatural cases, like brain-machine interfaces.

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 osing and treating disease and for improving brain/machine interfaces.

11 in the MRP corroborating its suitability for brain-machine interfaces, although information about gra

12 rtical states in freely behaving animals for brain-machine interface and delivered electrochemical sp

13 been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation th

14 ented this method in a real-time biofeedback brain-machine interface, and found that monkeys could le

15 ental for constructing computational models, brain-machine interfaces, and neuroprosthetics.

16 probes have led to advances in neuroscience, brain-machine interfaces, and treatment of neurological

17 Brain-machine interfaces are being developed to assist p

18 Brain-machine interfaces are not only promising for neur

19 ens the door for the direct wiring of robust brain-machine interfaces as well as for investigations o

20 This result is particularly important to brain-machine interfaces because it could enable stable

21 This technology is commonly referred to as a Brain-Machine Interface (BMI) and is achieved by predict

22 Here we use a brain-machine interface (BMI) based on real-time magneto

23 To evaluate whether a brain-machine interface (BMI) could be used to regain co

24 vements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of

25 m cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could

26 Studies on neural plasticity associated with brain-machine interface (BMI) exposure have primarily do

27 Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys

28 The results from recent brain-machine interface (BMI) studies suggest that it ma

29 ectrode array to rat's gustatory cortex with brain-machine interface (BMI) technology.

30 this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping p

31 re rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and

32 Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the h

33 as the brain-computer interface (BCI) or the brain-machine interface (BMI), are gaining momentum in t

34 de interest in the possibility of creating a brain-machine interface (BMI), particularly as a means t

35 ivity, we incorporate neural dynamics into a brain-machine interface (BMI).

36 Brain-machine interfaces (BMI) create novel sensorimotor

37 ation of brain-computer interfaces (BCI) and brain-machine interfaces (BMI) depends significantly on

38 ficant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstra

39 Much progress has been made in brain-machine interfaces (BMI) using decoders such as Ka

40 f the successful strategies for implementing brain-machine interfaces (BMI), by which the subject lea

41 Such brain machine interfaces (BMIs) have allowed animal subj

42 Brain-machine interfaces (BMIs) aim to help people with

43 Intracortical brain-machine interfaces (BMIs) aim to restore lost moto

44 an be used to predict reach intentions using brain-machine interfaces (BMIs) and therefore assist tet

45 responses are relevant to the development of brain-machine interfaces (BMIs) because they provide a r

46 Traditionally, brain-machine interfaces (BMIs) extract motor commands f

47 o control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impre

48 Although brain-machine interfaces (BMIs) have focused largely on

49 A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders

50 The field of invasive brain-machine interfaces (BMIs) is typically associated

51 Intracortical brain-machine interfaces (BMIs) may eventually restore f

52 Brain-machine interfaces (BMIs) offer the promise of rec

53 Brain-machine interfaces (BMIs) provide a framework for

54 Brain-machine interfaces (BMIs) provide a new assistive

55 Brain-machine interfaces (BMIs) should ideally show robu

56 ecessary for successful motor prostheses and brain-machine interfaces (BMIs).

57 siological principles and the development of brain-machine interfaces (BMIs).

58 omagnetic noise, an interesting modality for brain-machine interfaces (BMIs).

59 MC) or under direct neural control through a brain-machine interface (Brain Control, BC).

60 Brain-machine interfaces can allow neural control over a

61 It is hoped that brain-machine interfaces can be used to restore the norm

62 Brain-machine interfaces could provide a solution to res

63 The success of these brain-machine interfaces depends on the electrodes that

64 cortical control of movement as well as for brain-machine interface development.

65 new generation of diagnostic and therapeutic brain-machine interface devices.

66 o facilitate the development of long-lasting brain-machine interface devices.

67 se confirmed that our probes can form stable brain-machine interfaces for at least 2 months.

68 s and could be applied in the development of brain-machine interfaces for restoring speech in paralys

69 Brain-machine interfaces have great potential for the de

70 One type of brain-machine interface is a cortical motor prosthetic,

71 applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic com

72 population activities as required, e.g., in brain-machine interface paradigms.

73 spiking activity could control a biomimetic brain-machine interface reflecting ipsilateral kinematic

74 current circumstances.SIGNIFICANCE STATEMENT Brain-machine interfaces represent a solution for physic

75 ics, suggesting that accurate operation of a brain-machine interface requires recording from large ne

76 eriments, the neural control engineering and brain-machine interface studies.

77 control, it may serve in the development of brain machine interfaces that also use ipsilateral neura

78 Here we demonstrate a closed loop brain-machine interface that delivers electrical stimula

79 unctional skin-like electronics, and improve brain/machine interfaces that enable transmission of the

80 patterns have been used in the new field of brain-machine interfaces to show how cursors on computer

81 Brain-machine-interface training was done for 13 weeks w

82 Brain-machine interfaces use neuronal activity recorded

83 ng how subjects learned de novo to control a brain-machine interface using neurons from motor cortex.

84 utility of this internal model estimate for brain-machine interfaces, we performed an offline analys

85 The autonomous brain-machine interface would use M1 for both decoding m