Power-saving design opportunities for wireless intracortical brain–computer interfaces

Even-Chen, Nir; Muratore, Dante Gabriel; Stavisky, Sergey D.; Hochberg, Leigh R.; Henderson, Jaimie M.; Murmann, Boris; Shenoy, Krishna V.

doi:10.1038/s41551-020-0595-9

Cited by 73 publications

(73 citation statements)

References 103 publications

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“…There are three main sources of noise in neural recordings: electrode impedance thermal noise, tissue thermal noise and interface electronics noise 84 . Of the three, only interface electronics noise is likely to contain correlated power across different frequency bands.…”

Section: Methodsmentioning

confidence: 99%

The significance of neural inter-frequency power correlations

Savolainen

2021

Sci Rep

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It is of great interest in neuroscience to determine what frequency bands in the brain have covarying power. This would help us robustly identify the frequency signatures of neural processes. However to date, to the best of the author’s knowledge, a comprehensive statistical approach to this question that accounts for intra-frequency autocorrelation, frequency-domain oversampling, and multiple testing under dependency has not been undertaken. As such, this work presents a novel statistical significance test for correlated power across frequency bands for a broad class of non-stationary time series. It is validated on synthetic data. It is then used to test all of the inter-frequency power correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings in Macaque M1, using a very large, publicly available dataset. The recordings were Current Source Density referenced and were recorded with a Utah array. The results support previous results in the literature that show that neural processes in M1 have power signatures across a very broad range of frequency bands. In particular, the power in LFP frequency bands as low as 20 Hz was found to almost always be statistically significantly correlated to the power in kHz frequency ranges. It is proposed that this test can also be used to discover the superimposed frequency domain signatures of all the neural processes in a neural signal, allowing us to identify every interesting neural frequency band.

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Section: Methodsmentioning

confidence: 99%

The significance of neural inter-frequency power correlations

Savolainen

2021

Sci Rep

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“…Decoders built on multiunit thresholds can also be maintained over long periods [4], [7], [36] and population dynamics may be estimated from multiunit threshold crossings without spike sorting [37]. An analysis of iBCI decoding in monkeys and humans proposes that digital sampling at 1 kHz should be sufficient for iBCI applications [21]. These approaches could reduce the wireless bandwidth by roughly an order of magnitude, thereby simplifying the engineering design constraints for a fully implanted wireless iBCI or, alternatively, freeing bandwidth to record from thousands of electrode contacts [38]- [43].…”

Section: A Design Trade-offsmentioning

confidence: 99%

“…However, wireless recording for iBCI has yet to be demonstrated in people, in part because translating the proven cabled system (16 bits per sample at 30 kS/s for each of 96 electrodes per implanted array) to wireless form presents significant engineering challenges. Although wireless broadband signal acquisition might not be a design requirement for some targeted iBCI applications [ 21 – 23 ], broadcasting the entire broadband signal allows for investigation of novel iBCI algorithms spanning the full spectrum of neural activity while supporting a wide range of fundamental electrophysiological research during untethered use. With this motivation and the goal of enabling continuous, independent use of an iBCI, previous work from our team created a compact, power efficient neurosensor that digitizes and wirelessly transmits broadband cortical activity from a 96-channel chronically implanted microelectrode array [ 24 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia

Simeral

Hosman²,

Saab³

et al. 2021

IEEE Trans. Biomed. Eng.

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Objective. Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and pointand-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. Methods. Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial

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“…A common first approach when dealing with neuronal recording is to select a specific signal band that is typically for extracellular action potentials within 300 to 5 kHz so as to reduce both low and high frequency noise [27]. Here, as mentioned, a 2nd IIR Butterworth filters is used with a bandwidth of 300 Hz-3 kHz, to remove the low frequency noise characterized by LFP, the offset and the off-band noise.…”

Section: Band-pass Filtermentioning

confidence: 99%

A Low Power 1024-Channels Spike Detector Using Latch-Based RAM for Real-Time Brain Silicon Interfaces

Saggese

Strollo

2021

Electronics

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High-density microelectrode arrays allow the neuroscientist to study a wider neurons population, however, this causes an increase of communication bandwidth. Given the limited resources available for an implantable silicon interface, an on-fly data reduction is mandatory to stay within the power/area constraints. This can be accomplished by implementing a spike detector aiming at sending only the useful information about spikes. We show that the novel non-linear energy operator called ASO in combination with a simple but robust noise estimate, achieves a good trade-off between performance and consumption. The features of the investigated technique make it a good candidate for implantable BMIs. Our proposal is tested both on synthetic and real datasets providing a good sensibility at low SNR. We also provide a 1024-channels VLSI implementation using a Random-Access Memory composed by latches to reduce as much as possible the power consumptions. The final architecture occupies an area of 2.3 mm2, dissipating 3.6 µW per channels. The comparison with the state of art shows that our proposal finds a place among other methods presented in literature, certifying its suitability for BMIs.

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Power-saving design opportunities for wireless intracortical brain–computer interfaces

Cited by 73 publications

References 103 publications

The significance of neural inter-frequency power correlations

The significance of neural inter-frequency power correlations

Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia

A Low Power 1024-Channels Spike Detector Using Latch-Based RAM for Real-Time Brain Silicon Interfaces

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