- Open Access
Accuracy to detection timing for assisting repetitive facilitation exercise system using MRCP and SVM
© The Author(s) 2017
Received: 18 August 2017
Accepted: 31 October 2017
Published: 7 November 2017
This paper presents a feasibility study of a brain–machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient’s brain. We proposed a brain–machine interface system for automatically detecting motor commands and stimulating the paralyzed part of a patient. To determine motor commands from patient electroencephalogram (EEG) data, we measured the movement-related cortical potential (MRCP) and constructed a support vector machine system. In this paper, we validated the prediction timing of the system at the highest accuracy by the system using EEG and MRCP. In the experiments, we measured the EEG when the participant bent their elbow when prompted to do so. We analyzed the EEG data using a cross-validation method. We found that the average accuracy was 72.9% and the highest at the prediction timing 280 ms. We conclude that 280 ms is the most suitable to predict the judgment that a patient intends to exercise or not.
The number of cerebral stroke patients is increasing worldwide. For example, in Japan, cerebral stroke patients exceeded 2.8 million people in 2015 . Patients often suffer from aftereffects following a stroke, the most frequent of which is hemiplegia. To recover motor function following hemiplegia, patients must endure a long course of difficult rehabilitation. Many studies have investigated methods to shorten the recovery time through efficient rehabilitation after hemiplegia [2, 3].
Neurorehabilitation has been shown to be very efficient [4, 5]. Neurorehabilitation is a method to prompt the recovery of the injured neural system. Repetitive facilitation exercise is drawing attention as a particularly effective rehabilitation. Kawahira demonstrated the efficacy of repetitive facilitation exercise . Patients using repetitive facilitation exercise can recover motor control three times faster than those using usual therapy . Repetitive facilitation exercise also improves the paralysis part to health compared with usual therapy .
The most important point of repetitive facilitation exercise is the stimulation timing. It is necessary for the stimulation to occur before the motor command reaches the spinal cord for success of the repetitive facilitation exercise. However, it is difficult to successfully perform repetitive facilitation exercise because even a skilled therapist cannot detect the timing of the motor command. The success of repetitive facilitation exercise is dependent on the intuition and experience of the therapist.
Even an experienced therapist cannot achieve a 100% success rate with the repetitive facilitation exercise because therapists cannot detect patient motor intentions. It has been reported that five of twelve patients showed increased motor evoked potential after repetitive facilitation exercise by therapists . This shows that the nervous systems of these patients underwent some reconstruction. Although there are individual differences, it is said that the conventional success rate is about five of twelve, that is just 45%. To improve the higher success rate than the target value 45%, therapists require a system to assist repetitive facilitation exercise. An upper limb reaching device has been proposed to reduce fatigue and pain in the paralyzed arm and to decrease the burden on the therapist . The patient repeats inward and outward movements to push the front and back buttons alternately. When the patient pushes the button, the device generates vibrations and electric stimulation to make it easier to move the paralyzed arm. However, the device cannot control the accurate stimulation timing because the device cannot detect the motor command generated in the brain.
There have been many studies of real time detecting the motor command in brain. Most of these have used electroencephalogram (EEG) because it has a higher time resolution than other brain measurement devices. EEG is simple to analyze in real time. For example, Lucian reported that EEG can show the steering timing of a driver during driving [10, 11, 12]. He measured and analyzed EEG data while participants operated a driving simulator. Using this method, the turn direction was detected 811 ms before steering with an accuracy of 74.6%. In another study, Choi showed that a brain–machine interface system using EEG could be used to control a wheelchair . This system analyzed EEG data and moved forward or turned left or right based on the measured EEG signals within 125 ms.
These studies are useful for realizing real-time brain–machine interfaces for healthy individuals. However, these studies have not been adapted to the rehabilitation.
Three healthy participants (male, age 22–23, two right-handed and one left-handed) were enrolled in the experiment. We did not enroll paralyzed patients because the aim of the present study was only to validate the proposed method for detecting motor commands. Informed consent was obtained from all participants. All participants attested to having slept well the night before the experiment to exclude the influence of sleep deprivation. All participants did not intake the drugs such as caffeine, alcohol, nicotine, and other medicines. The experiments were approved by the Waseda University Institutional Review Board (No. 2014-156).
Device settings of a g. USBamp and ACL300
Sampling rate Hz
Analog input channel ch
Acceleration sensor ch1
Acceleration sensor ch2
Acceleration sensor ch3
We used a three-degree-of-freedom accelerometer (ACL300, Biometric Inc., USA) to detect the time when the participant moved their arm. We affixed the accelerometer to the participant’s wrist as shown in Fig. 6 and connected it to an analog input–output board (AIO-163202FX-USB, Contec Inc., USA) to get the analog input value. The accelerometer used three channels, as shown in Table 1.
We conducted the experiment in a closed room to minimize noise disturbances. During the experiment, the participant did not talk and sat still. In addition, we asked the participant to try to avoid swallowing saliva or blinking hard. The temperature was 20 ± 15 °C, and the humidity was 45–85%.
The experimental condition is the prediction timing. The prediction accuracy changes as to the timing before action. However, the timing is influenced by the recognition delay. Even the participant bent his/her elbow as soon as the disappearance of the bar, and there is actually the delay because it takes time to recognize it. The recognition delay is said to about 200 ms, but there is no clarity of accurate delay. In this paper, the experimental condition is the prediction timing around 200 ms before action timing. The condition is 70, 140, 210, 280 and 350 ms before action timing.
Movement-related cortical potential (MRCP)
We focused on the functions of EEG. For example, event-related potential is the electric fluctuation detected from neurons following light or sound stimulus . The P300 speller, a communication device for severely paralyzed patients, utilizes the event-related potential function. Event-related desynchronization is another function in which the power spectrum of the EEG alpha band decreases following motor commands . Event-related desynchronization is often used in rehabilitation systems. In the present study, we used movement-related cortical potential (MRCP). MRCP is the change in EEG signal resulting from the plan and action of voluntary exercise [16, 17]. MRCP is detectable before and after exercise. In particular, MRCP that starts about 800 ms before exercise is called the motor readiness potential. We hypothesized that the motor readiness potential would show the timing of when a motor command occurs in the brain.
Support vector machine (SVM)
To detect MRCP, a pattern identification unit is required. There are two kinds of pattern identification unit: One uses a parametric method for which the probability distribution of data is known in advance and another uses a nonparametric method which requires collected data because the probability distribution of data is unknown. We used the nonparametric method because EEG data are different for each patient.
We employed a support vector machine (SVM) because it can divide known data into two classes [18, 19, 20]. Compared with other algorithms, SVM is suitable to judge the two classes that the human tends to move his body or not. Using this SVM system, we divided the EEG data into data during rest and data during action. The system needs to detect EEG data during action as a motor command.
Judgment of movement from acceleration
To detect motor commands, we used EEG data from 0 to 2 s during each measurement session as the feature quantity during rest. EEG data at 210 ms after the acceleration of the wrist were considered the threshold for motor command. There are two reasons to set this threshold to 210 ms: One is the human cognitive delay. In this experiment, the participant bent and their elbow based on a signaling displayed on a monitor—the disappearance of the red bar. Therefore, we considered that it would take 200 ms after the bar disappears for the participant to recognize the bar disappearance. Another reason is machine delay. There is a machine delay of 10 ms from motion intention detection by the SVM to actuation of the FES. From the above, we set the expected delay to 210 ms.
To compare with other detection timings, we validate the discrimination rate by each 70 ms via cross-validation. The condition is 70, 140, 210, 280, 350 ms.
Results and discussion
Experimental result of detection rate
Detection rate for shift time r %
Average all subjects
Using EEG data 210 ms after the red bar disappeared, all discrimination data were at least 67%. This result was above the 45% target value. This indicates that using EEG data sorted by SVM, the proposed system can perform FES on paralyzed patients with adjustable timing. Repetitive facilitation exercise administered using the proposed EEG system is potentially more successful than that administered by a therapist.
In the present study, we carried out the experiment by only three participants. We should conduct experiments using more participants. In addition, the EEG data were collected from only healthy subjects. For some stroke patients, although the neural system is different from the healthy subject, the sensory recognition motor loops would be same as to healthy because the neural system cannot feedback but can feedforward. We should validate the detection using EEG signals of paralyzed patients compared with the healthy people. In future work, we will develop the system using FES.
In the present study, we proposed a brain–machine interface system to assist repetitive facilitation exercise. As a result, the average accuracy was 72.9% and the highest at the prediction time 280 ms. We conclude that 280 ms is the most suitable to predict the judgment that a patient intends to exercise or not. In future work, we will develop this repetitive facilitation exercise assistance system.
SM conceived the study and drafted the manuscript. JT carried out all experiments and analyzed the data. KY and MF participated in the research design and sequence alignment. All authors read and approved the final manuscript.
The authors of this paper would like to thank Waseda University in Japan by offering the funding support for this publication.
The authors declare that they have no competing interests.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article.
This research was supported in part by the Outstanding Graduate COE Support Subsidy “Global Robot Academia (GRA)” from MEXT; in part by JSPS KAKENHI under Grant Numbers JP16H07265, JP14J07226, 26242061; and in part by the Council for Science, Technology and Innovation (CSTII), Cross-ministerial Strategic Innovation Promotion Program (SIP) (funding agency: JST).
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