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

Hendrik Wöhrle, Marc Tabie, Su Kim, Frank Kirchner, Elsa Kirchner
2017 Sensors  
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
more » ... sis 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. To achieve this, the adaptation of robotic rehabilitation systems (e.g., active orthoses or exoskeletons) [14-22] with real-time analysis of physiological data related to neuronal or muscular activity to predict movements seems to be a promising approach [23] [24] [25] [26] [27] [28] [29] [30] . Data that can be acquired non-invasively from the human body, for instance surface Electroencephalography (EEG) and surface Electromyography (EMG), have a range of advantages for practical purposes, e.g., they provide a good time resolution, the data acquisition equipment is relatively cheap and portable. Several studies could show that the approach to adapt assistive or therapeutic devices with respect to physiological data motivates the patients and encourages the active participation in the exercises, which can result in an increased neuroplasticity in, e.g., stroke rehabilitation and, thus, therapy success [31] [32] [33] [34] [35] [36] . One important aspect to achieve practical usability of a therapeutic device is to provide a high mobility [37, 38] . This can be achieved by the development of small computing systems that control the device, but also process the physiological data and are embedded into the rehabilitation device or other equipment like wheelchairs [39] and exoskeletons [29] . However, systems like Brain-Computer Interface (BCI) are nowadays often used in artificial and stationary experimental setups. Reasons for this are (1) the immobility of the utilized hardware for data acquisition, (2) the experimental conditions in which BCI systems are still employed, but also (3) the size of the computing devices required for the analysis of the human EEG or EMG. Recently, substantial progress has been achieved regarding (1) and (2) (see Section 2.4). Although the development of highly performant and power-efficient miniaturized computing devices is an important aspect for rehabilitation applications, aspect (3) has rarely been in the focus of previous research. For many rehabilitation applications, four different types of requirements are important for the development of embedded systems. First, the system should be as small as possible and have a low power consumption. Secondly, a computing device that is embedded into a larger rehabilitation device must provide the ability to communicate with various other systems and provide additional features such as the possibility to record data. These "high-level" functionalities are very diverse in structure and their implementation is usually based on a wide range of different software libraries. Fortunately, apart from certain types of communication (e.g., motor control to actuate a device), these functionalities are mostly not time-critical. Hence, these features are well-suited for a realization in software. and can be performed by a generic Central Processing Unit (CPU). Thirdly, for the actual processing of the physiological data, a set of different signal processing and Machine Learning (ML) algorithms has to be applied on the incoming stream of physiological data in real-time. Depending on the amount of data and the complexity of the algorithms, this part can be computationally expensive. If we aim to use movement prediction for the control of an assistive or therapeutic device physiological signals must be chosen that provide this information. However, their analysis should be performed with minimal latency. Fourthly, if different physiological signals are combined to predict movements in a hybrid approach, multiple different signal processing operations have to be applied asynchronously and in parallel to different streams of data. This can be a problem if the processing should be performed in miniaturized processing devices. In this paper, we propose a hybrid Field Programmable Gate Array (FPGA)-based approach for the implementation of a hybrid BCI, since this can fulfill the requirements discussed above. Our system is based on the Xilinx Zynq ® [40] , that combines a generic CPU with programmable logic (which is equivalent to an FPGA). This combination makes it possible to implement high-performance processing devices, which are power-efficient, have small dimensions, and can replace a bulky stationary PC. The CPU of the system offers the possibility to use standard software for user interaction as well as high-level and configuration tasks, which are tasks that are usually not performance-critical. Computationally expensive and time-critical operations can be transferred to hardware-accelerators that are implemented using the FPGA, which assists the generic CPU. Multiple hardware accelerators can work independently and asynchronously on different tasks, e.g., to process multiple physiological signals at the same time. Since FPGAs offer the capability to exploit massive parallelism to accelerate computations, they represent a well-suited technology for the implementation of performance-critical
doi:10.3390/s17071552 pmid:28671632 pmcid:PMC5539567 fatcat:2ysi5zwe5fdhnmp3gfyz7xpvby