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Review
. 2022 Oct 7;9(10):nwac212.
doi: 10.1093/nsr/nwac212. eCollection 2022 Oct.

Deep brain-machine interfaces: sensing and modulating the human deep brain

Yanan Sui  1 , Huiling Yu  1 , Chen Zhang  1 , Yue Chen  1 , Changqing Jiang  1 , Luming Li  1   2   3   4
Affiliations
Review

Deep brain-machine interfaces: sensing and modulating the human deep brain

Yanan Sui et al. Natl Sci Rev. .

Abstract

Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.

Keywords: deep brain stimulation; deep brain–machine interface; sensing and modulation; stereotactic electroencephalography.

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Figures

Figure 1.
Figure 1.
Common approaches for sensing and modulating the human brain with depth distribution and spatial resolution. The horizontal axis lists major electrical, magnetic, ultrasonic and optical approaches including EEG (electroencephalogram), ECoG (electrocorticography), EA (endovascular approach), TES (transcranial electrical stimulation), sEEG (stereotactic electroencephalography), DBS (deep brain stimulation), MER (microelectrode recording), MEA (microelectrode array), TMS (transcranial magnetic stimulation), MEG (magnetoencephalography), fMRI (functional magnetic resonance imaging), TUS (transcranial ultrasound), fUS (functional ultrasound), fNIRS (functional near-infrared spectroscopy) and laser therapy. The vertical axis represents the depth range covered by each approach from surface to deep brain. Color represents the scale of spatial resolution, with opacity illustrating the application rate of each approach at different depth levels. EEG, ECoG, MEA, MEG, fMRI, fUS and fNIRS are mainly used for sensing, whilst sometimes stimulation could also be delivered with ECoG and MEA electrodes. TES, TMS, TUS and laser therapy are mainly for modulation. EA, sEEG, DBS and MER can be used in both modalities.
Figure 2.
Figure 2.
Deep brain structures and their main functions.
Figure 3.
Figure 3.
Major electrical recording and stimulation approaches for brain–machine interfaces. EEG, ECoG and MER for recording; TES for stimulation; DBS and sEEG for both recording and stimulation.
Figure 4.
Figure 4.
Sensing and modulation via a deep brain–machine interface. The implanted deep brain stimulator can record LFP signals and apply stimulation based on the sensing signals and the control policy. Control policies for current closed-loop deep brain stimulation can be categorized as Bang-Bang control, PID control and model predictive control.
See this image and copyright information in PMC

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