Towards a Novel HMI Paradigm Based on Mixed EEG and Indoor Localization Platforms

Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and machine Learning (ML) near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multiprecision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAP's form factor is 16x21x14 mm 3 and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an accelerometer; and a shield including an analog front-end (AFE) for ExG acquisition. Finally, the system also includes a flexibly placeable photoplethysmogram (PPG) PCB with a size of 9x7x3 mm 3 and a rechargeable battery (ϕ 12x5 mm 2 ). We demonstrate BioGAP on a Steady State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We achieve 3.6 µJ/sample in streaming and 2.2 µJ/sample in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW allowing for an operation time of 15 h.

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A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

Salvaro

Benatti

Kartsch

et al. 2019

IEEE Internet Things J.

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A growing trend in Human Computer Interaction (HCI) is to integrate computational capabilities into wearable devices, to enable sophisticated and natural interaction modalities. Acting directly by decoding neural activity is a very natural way of interaction and one of the fundamental paradigms of Brain Computer Interfaces (BCIs) as well. In this work we present a wearable IoT node designed for BCI spelling. The system is based on Visual Evoked Potentials detection and runs the Canonical Correlation Analysis (CCA) on a low power microcontroller. Neural data is acquired by an array of EEG active dry electrodes, suitable for a minimally intrusive interface. To evaluate our solution, we optimized the system on eight subjects and tested it on five different subjects for four and eight stimuli, reaching a peak transfer rate of 1.57 bps, comparable with those achieved by state-of-the-art non-embedded systems. The power consumption of the device is less than 30 mW, resulting in 122 hours of operation with a standard 1000 mAh battery. I. INTRODUCTION Brain Computer Interfaces (BCIs) for Human-Computer Interaction (HCI) were first developed to support people with disabilities in their interaction with the external world, with one of the first successful examples being BCI spellers. Recent years have seen BCI applications reach out to a larger set of scenarios, such as industry, gaming, learning, healthcare [1] and rehabilitation [2]. Several tech companies developing consumer-oriented products (Google, Apple, Facebook, etc.) have also become active in this field [3], [4], [5], with the vision of being able to substitute traditional HCIs based on conventional computer input devices, gesture and voice recognition, touch-screen interaction [6], [7], [8], with the possibility to directly interact and control computers with our brain signals. Bringing this fascinating idea into life will be a tremendous boost towards integrating actions and interactions with objects in a fully-connected IoT scenario. Applications can range from verifying whether a worker is attending a specific task or effectively receiving a communication for safety purposes, to remotely control devices in industrial or home

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Towards a Novel HMI Paradigm Based on Mixed EEG and Indoor Localization Platforms

Cited by 4 publications

References 18 publications

A Wearable Device for Brain–Machine Interaction with Augmented Reality Head-Mounted Display

A Wearable Device for Brain–Machine Interaction with Augmented Reality Head-Mounted Display

BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing

A Minimally Invasive Low-Power Platform for Real-Time Brain Computer Interaction Based on Canonical Correlation Analysis

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