Publikation
A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction
Hendrik Wöhrle; Marc Tabie; Su-Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
In: Sensors - Open Access Journal (Sensors), Vol. 17, No. 7, Page 1552, MDPI, 7/2017.
Zusammenfassung
A current trend in the development of assistive devices for rehabilitation, for example
exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and
usability, for example by predicting the patient’s upcoming movements using electroencephalography
(EEG) or electromyography (EMG). However, these modalities have different temporal properties
and classification accuracies, which results in specific advantages and disadvantages. To use
physiological data analysis in rehabilitation devices, the processing should be performed in real-time,
guarantee close to natural movement onset support, provide high mobility, and should be performed
by miniaturized systems that can be embedded into the rehabilitation device. We present a
novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction
using physiological data. Its parallel processing capabilities allows the combination of movement
predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked
by instructions of the therapist. The system is evaluated in an offline and an online study with
twelve healthy subjects in total. We show that it provides a high computational performance and
significantly lower power consumption in comparison to a standard PC. Furthermore, despite the
usage of fixed-point computations, the proposed system achieves a classification accuracy similar to
systems with double precision floating-point precision.