BioWolf: A Sub-10-mW 8-Channel Advanced Brain–Computer Interface Platform With a Nine-Core Processor and BLE Connectivity

Kartsch, Victor; Tagliavini, Giuseppe; Guermandi, Marco; Benatti, Simone; Rossi, Davide; Benini, Luca

doi:10.1109/tbcas.2019.2927551

Cited by 42 publications

(29 citation statements)

References 43 publications

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“…An energy-efficient wearable system for general BCI "BioWolf" was presented in Ref. [1]. The BioWolf features three main components, including a well-designed parallel ultra-low power defined SoC MCU for signal processing, a Nordic ARM-SoC MCU for BLE communications and interface management, and an AFE for bio-signal acquisition from Texas Instruments.…”

Section: Multi-core Processor Platform For Generalpurpose Wearable Bcmentioning

confidence: 99%

“…1 in Ref. [1], a complete state-of-the-art wearable EEG system can have the following key components that facilitate the EEG flow.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, depending on the computational paradigms, feature extraction and classification of the signal may require complex algorithms. If not efficiently implemented and executed on high-end hardware, the algorithm may fail to satisfy real-time timing enclosure or consume too much energy in portable battery-operated devices [1].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, wearable systems with artificial intelligence (AI) based self-learning computation and adaptivity are becoming a trend in medical/commercial EEG products. Big tech companies such as Apple, Google, and Samsung have marketed commercial products and actively researching and designing solutions to promote the next-generation of wearable brain-computer interface (BCI) [1]. Although medical information can be obtained from the human body, neural activity has gained more attention because it can provide a direct BCI.…”

Section: Introductionmentioning

confidence: 99%

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An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation

Shi

Zhang

et al. 2020

Brain Science Advances

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Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface makes it possible to place the analog front‐end circuits within a tiny packaged chip; therefore, enabling high‐count EEG acquisition channels. This article introduces and reviews the state‐of‐the‐art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.

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Section: Multi-core Processor Platform For Generalpurpose Wearable Bcmentioning

confidence: 99%

“…1 in Ref. [1], a complete state-of-the-art wearable EEG system can have the following key components that facilitate the EEG flow.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation

Shi

Zhang

et al. 2020

Brain Science Advances

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“…In particular, energy efficiency and scalability (i.e., according to the specific pathology characteristics of each patient) are important factors to take into account in any wearable sensor design [8] for personalized remote long-term health monitoring [5]- [7], [9]- [11]. Modern ultra-low power (ULP) platforms [12]- [16] can offer many advantages in terms of parallelization capabilities that can be exploited in biomedical applications. Moreover, these platforms include direct memory access (DMA) modules that are more efficient than the main processors in data transfers.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

Giovanni

Valdés

Peon

et al. 2021

IEEE Trans. Emerg. Topics Comput.

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In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel computing capabilities that can enable prevention diagnosis and treatment of diseases in ambulatory or home-based setups. In this paper, we explore the benefits of the parallelization capabilities and computing heterogeneity of new wearable sensors in the context of a personalized online atrial fibrillation (AF) prediction method for daily monitoring. First, we apply optimizations to a single-core design to reduce energy, based on patient-specific training models. Second, we explore multi-core and memory banks configuration changes to adapt the computation and storage requirements to the characteristics of each patient. We evaluate our methodology on the Physionet Prediction Challenge (2001) publicly available database, and assess the energy consumption of single-core (ARM Cortex-M3 based) and new ultra-low power multi-core architectures (opensource RISC-V based) for next-generation of wearable platforms. Overall, our exploration at the application level highlights that a parallelization approach for personalized AF in multi-core wearable sensors enables energy savings up to 24 % with respect to single-core sensors. Moreover, including the adaptation of the memory subsystem (size and number of memory banks), in combination with deep sleep energy saving modes, can overall provide total energy savings up to 34 %, depending on the specific patient.

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Implantable and Wearable Sensors for Assistive Technologies

Güler¹,

Tufan²,

Chakravarti³

et al. 2023

Encyclopedia of Sensors and Biosensors

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BioWolf: A Sub-10-mW 8-Channel Advanced Brain–Computer Interface Platform With a Nine-Core Processor and BLE Connectivity

Cited by 42 publications

References 43 publications

An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation

An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

Implantable and Wearable Sensors for Assistive Technologies

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