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

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

【Source】IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS

【影响因子】5.234

【Abstract】Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40mm 20 mm 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31mW, providing up to 38h operation (65mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI.

【Keywords】Blockchain; Protocols; Privacy; Charging stations; Electric vehicle charging; Public key; Elliptic curves; Body sensor networks; brain-computer interfaces; digital signal processing; electroencephalography; internet of things; low-power electronics; multicore processing

【发表时间】2019 OCT

【收录时间】2022-01-02

【文献类型】

【主题类别】

--