9,819 research outputs found
An embedded system for evoked biopotential acquisition and processing
This work presents an autonomous embedded system for evoked biopotential acquisition and processing. The system is versatile and can be used on different evoked potential scenarios like medical equipments or brain computer interfaces, fulfilling the strict real-time constraints that they impose. The embedded system is based on an ARM9 processor with capabilities to port a real-time operating system. Initially, a benchmark of the Windows CE operative system running on the embedded system is presented in order to find out its real-time capability as a set. Finally, a brain computer interface based on visual evoked potentials is implemented. Results of this application recovering visual evoked potential using two techniques: the fast Fourier transform and stimulus locked inter trace correlation, are also presented.Fil: Garcia, Pablo Andres. Universidad Nacional de la Plata. Facultad de IngenierÃa. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Spinelli, Enrique Mario. Universidad Nacional de la Plata. Facultad de IngenierÃa. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Toccaceli, Graciela Mabel. Universidad Nacional de la Plata. Facultad de IngenierÃa. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; Argentin
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
This paper presents an accurate and robust embedded motor-imagery
brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet,
matches the requirements of memory footprint and computational resources of
low-power microcontroller units (MCUs), such as the ARM Cortex-M family.
Furthermore, the paper presents a set of methods, including temporal
downsampling, channel selection, and narrowing of the classification window, to
further scale down the model to relax memory requirements with negligible
accuracy degradation. Experimental results on the Physionet EEG Motor
Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and
65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global
validation, outperforming the state-of-the-art (SoA) convolutional neural
network (CNN) by 2.05%, 5.25%, and 5.48%. Our novel method further scales down
the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory
footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The
scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and
consuming 4.28mJ per inference for operating the smallest model, and on a
Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model,
enabling a fully autonomous, wearable, and accurate low-power BCI
TOBE: Tangible Out-of-Body Experience
We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing
the inner states of users using physiological signals such as heart rate or
brain activity. Tobe can take the form of a tangible avatar displaying live
physiological readings to reflect on ourselves and others. Such a toolkit could
be used by researchers and designers to create a multitude of potential
tangible applications, including (but not limited to) educational tools about
Science Technologies Engineering and Mathematics (STEM) and cognitive science,
medical applications or entertainment and social experiences with one or
several users or Tobes involved. Through a co-design approach, we investigated
how everyday people picture their physiology and we validated the acceptability
of Tobe in a scientific museum. We also give a practical example where two
users relax together, with insights on how Tobe helped them to synchronize
their signals and share a moment
The Strathclyde Brain Computer Interface (S-BCI) : the road to clinical translation
In this paper, we summarise the state of development of the Strathclyde Brain Computer Interface (S-BCI) and what has been so far achieved. We also briefly discuss our next steps for translation to spinal cord injured patients and the challenges we envisage in this process and how we plan to address some of them. Projections of the S-BCI project for the coming few years are also presented
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