A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

Abstract

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.

Modular Design and Decentralized Control of the Recupera Exoskeleton for Stroke Rehabilitation

“…This will enable additional mobility features on the system such as active balancing and walking. Furthermore, although the system offers the possibility to enhance the control using biosignal-based control as discussed in [36,50,56,57], these aspects have not been evaluated in depth. Accordingly, in future we will evaluate the effect of biosignals on the control of the system and the effectiveness in stroke therapy.…”

“…A ZC7030 consists of two parts: a Processing System (PS) (which is an ARM ® Cortex-A9 dual core CPU running at 1 GHz) and a Programmable Logic (PL) section, which is equivalent to an FPGA. 512 MB DDR3 SD-RAM are connected to the PS [50]. For communication, each ZynqBrain provides 5 duplex high-speed low-voltage differential signaling (LVDS) connections to connect the ACUs (for details see [40]).…”

“…Additionally, a web server is running on the second ZynqBrain providing access to a web application written in JavaScript and provides a user interface for the therapist or patient, e.g., to select a mode, adjust settings or observe the current state of the exoskeleton (see Section 3.2.3). Furthermore, some functionalities can be triggered by biosignals like EEG or EMG as the biosignal processing can be done on the embedded processors [36,50].…”

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection—Relevance for Neuroscience and Clinical Applications

“…Here we showed that during complex visual-motor activity it is possible to detect ERP activity related with target recognition and task-change processes. This can be used to improve the movement prediction when patients are instructed to execute specific movements by a therapist or serious game (Wöhrle et al, 2017 ). Our results support that for strong activities such as P300 and related processes single-trial ERP detection is in principle making use of features that are physiologically interpretable and expected.…”

“…For example, the task of a patient in a therapeutic intervention is to try to move the affected arm. This task can be commanded by an event in a serious game for rehabilitation or given by the therapist (Wöhrle et al, 2017 ). Based on the strength of P300 expression, the strain on the patient could be estimated and the game automatically be adapted.…”