A chronic generalized bi-directional brain-machine interface - PubMed (original) (raw)

A chronic generalized bi-directional brain-machine interface

A G Rouse et al. J Neural Eng. 2011 Jun.

Abstract

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

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Figures

Fig. 1

Fig. 1

Abstracted therapy control loops in a bi-directional neural interface.

Fig. 2

Fig. 2

Spectral band fluctuations for (a) a typical LFP of a patient with Parkinson’s disease in on and off states, (b) motor intention tuning signals for BMI prosthesis control. The red color illustrates power increases in the high gamma for neural prosthesis control.

Fig. 3

Fig. 3

Trade-offs of different sensing modalities for the BMI system.

Fig. 4

Fig. 4

Functional block diagram for bi-directional neural interface system.

Fig. 5

Fig. 5

(a) General device architecture for a bi-directional neural interface system using existing technology. (b) Equivalent electrical circuit.

Fig. 6

Fig. 6

Electrical device architecture and system partition for the prototype.

Fig. 7

Fig. 7

Brain activity sensing interface IC (BASIC) die photo with simplified block diagram.

Fig. 8

Fig. 8

MEMS acceleration sensor with capacitance-to-voltage interface (C/V) integrated circuit. The final sensor is encapsulated for manufacturing.

Fig. 9

Fig. 9

Partitioning for signal processing and algorithms.

Fig. 10

Fig. 10

General detection scheme.

Fig. 11

Fig. 11

Physical prototype of the implantable bi-directional BMI system.

Fig. 12

Fig. 12

One dimensional motor neuroprosthetic control test. (a) Temporal illustration of target selection task. The cursor is centered, desired target appears, subject moves the cursor using its brain signal, and rests during an inter-trial interval (with possible reward), (b) signal path illustration.

Fig. 13

Fig. 13

a) The percentage of correct trials for the two conditions of laboratory control (75–105 Hz) and time-domain sampling. Percentages are the total correct targets out of the number of trials where one of the two targets was selected. b) Mean movement time to select one of the targets for the same two conditions. c) Percentage correct for control (80–96 Hz) and on-board power estimation. d) Mean movement time for the two conditions.

Fig. 14

Fig. 14

Histograms of the signal amplitudes for trials where the desired target was left (red) and right (blue). The four conditions are a) control (75–105 Hz), b) time-domain mode, c) control (80–96 Hz), and d) power mode.

Fig. 15

Fig. 15

Receiver operating characteristic (ROC) curve for the different conditions. For a series of thresholds, the curve traces out the percentage of left target trials above the threshold versus the number of right target trials above the same threshold. a) Control and time-domain. b) Control and power estimation.

Fig. 16

Fig. 16

Sensing capability in the presence of 145Hz stimulation.

References

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