Balancing Specialized Versus Flexible Computation in Brain–Computer Interfaces

Karageorgos, Ioannis; Sriram, Karthik; Vesely, Jan; Lindsay, Nick; Wen, Xiayuan; Wu, Michael; Powell, Marc; Borton, David A.; Manohar, Rajit; Bhattacharjee, Abhishek

doi:10.1109/mm.2021.3065455

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…A key takeaway from Sections II-B and II-C is the need for flexible support of compute on emerging BCIs. Indeed, this is a topic explored in recent work on the HALO architecture for BCIs [23,69,70]. Prior to HALO, power efficiency was achieved by specializing BCIs to offer a specific type of computation for a specific brain region.…”

Section: Flexibility As a Goal In Brain-computer Interface Designmentioning

confidence: 99%

A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

Sriram¹,

Pothukuchi²,

Michał³

et al. 2023

Preprint

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Hull 1 is an accelerator-rich distributed implantable brain-computer interface (BCI) that reads biological neurons at data rates that are 2-3 orders of magnitude higher than the prior art, while supporting many neuroscientific applications. Prior approaches have restricted brain interfacing to tens of megabits per second in order to meet two constraints necessary for effective operation and safe long-term implantation-power dissipation under tens of milliwatts and response latencies in the tens of milliseconds. Hull also adheres to these constraints, but is able to interface with the brain at much higher data rates, thereby enabling, for the first time, BCI-driven research on and clinical treatment of brain-wide behaviors and diseases that require reading and stimulating many brain locations. Central to Hull's power efficiency is its realization as a distributed system of BCI nodes with accelerator-rich compute. Hull balances modular system layering with aggressive cross-layer hardware-software co-design to integrate compute, networking, and storage. The result is a lesson in designing networked distributed systems with hardware accelerators from the ground up.

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Section: Flexibility As a Goal In Brain-computer Interface Designmentioning

confidence: 99%

A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

Sriram¹,

Pothukuchi²,

Michał³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Designing microelectronic circuits and systems for medical implants and electroceuticals is challenging and bounded by several constraints, such as area dimensions, energy consumption, safety, and the need for continuous or very regular data telemetry [14]. While it is not difficult to find medical devices with different capacities of performing on-chip analogue or digital signal processing, active on-chip learning use in the neuromodulation or neuromonitoring domain is in its infancy [15]. Figure 1 demonstrates four general types of loops in medical devices, as applied to neurotechnologies (figure 1a).…”

Section: Novelty and Significancementioning

confidence: 99%

Weak self-supervised learning for seizure forecasting: a feasibility study

et al. 2022

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This paper proposes an artificial intelligence system that continuously improves over time at event prediction using initially unlabelled data by using self-supervised learning. Time-series data are inherently autocorrelated. By using a detection model to generate weak labels on the fly, which are concurrently used as targets to train a prediction model on a time-shifted input data stream, this autocorrelation can effectively be harnessed to reduce the burden of manual labelling. This is critical in medical patient monitoring, as it enables the development of personalized forecasting models without demanding the annotation of long sequences of physiological signal recordings. We perform a feasibility study on seizure prediction, which is identified as an ideal test case, as pre-ictal brainwaves are patient-specific, and tailoring models to individual patients is known to improve forecasting performance significantly. Our self-supervised approach is used to train individualized forecasting models for 10 patients, showing an average relative improvement in sensitivity by 14.30% and a reduction in false alarms by 19.61% in early seizure forecasting. This proof-of-concept on the feasibility of using a continuous stream of time-series neurophysiological data paves the way towards a low-power neuromorphic neuromodulation system.

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Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure Forecasting

Yang

Truong

Eshraghian

et al. 2021

Preprint

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A high performance event detection system is all you need for some predictive studies. Here, we present AURA: an Adaptive forecasting model trained with Unlabeled, Real-time data using internally generated Approximate labels on-the-fly. By harnessing the correlated nature of time-series data, a pair of detection and prediction models are coupled together such that the detection model generates labels automatically, which are then used to train the prediction model. AURA relies on several simple principles and assumptions: (i) the performance of an event prediction/forecasting model in the target application remains below the performance of an event detection model, (ii) detected events are treated as weak labels and deemed reliable enough for online training of a predictive model, and (iii) system performance and/or system responsive feedback characteristics can be tuned for a subject-under-test. For example, in medical patient monitoring, this enables personalization of the forecasting model. Seizure prediction is identified as an ideal test case of AURA, as preictal brainwaves are patient-specific and tailoring models to individual patients can significantly improve forecasting performance. AURA is used to generate an individual forecasting model for 5 patients, showing an average relative improvement in sensitivity by 33.33% and reduction in false alarms by 13.62%.

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Balancing Specialized Versus Flexible Computation in Brain–Computer Interfaces

Cited by 6 publications

References 11 publications

A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

Weak self-supervised learning for seizure forecasting: a feasibility study

Adaptive, Unlabeled and Real-time Approximate-Learning Platform (AURA) for Personalized Epileptic Seizure Forecasting

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