As the EEG independent component analysis, EEGLAB was used. The EEG signal was first notch filtered (60+/-0.5Hz as well as 120+/-0.5Hz bandwidth) to exclude artifacts from electrical current, which was in turn band-pass filtered with the frequencies between 0.04 to 200 Hz (FIR filtering). Note spatial care was taken to minimize the notch filter’s artifact in determining an optimal bandwidth.
4.4 Results
Figure 9 depicts the FTM outputs and thresholds, triggering position, and rotation angle of the system.
Fig. 9 Output value and response of ankle rehabilitation devise
When the output value of FTM surpasses the threshold, a trigger was generated, and the rehabilitation device was operated. Note the system was programmed as such that the rotation angle was kept constant for 20 seconds once the trigger is generated. Extra triggers during this period was neglected. After the 20 second period, another trigger could successfully operate the system.
Next, we compare the results of ICA analysis by the topograph display on the state of passive exercise performed independently of the patient's intention which is generally performed and the state where rehabilitation training by L-FTM developed in this study was performed. In this display, the part where the brain is activated is displayed in red.
Results of ICA analysis indicates that overall EEG power was stronger during the test period when ankle rehabilitation system using L-FTM was employed during motor imagery (Figure 10 left one) as compared to the period during the passive motion task (Figure 10 right one). Namely, the activity around Cz was more conspicuous the movement with motor imagery. This demonstrates that the brain signals of EEG increased with motor imagery, which was utilized to the rehabilitation system to be operated.
Fig.10 ICA results
5. Conclusions
We developed a neuro rehabilitation system that reads exercise intention and operates rehabilitation equipment without detecting specific EEG features unlike conventional methods targeting ankle joints.
・Without detecting ERD and ERS, using the L-FTM based on the two labels of EEG power 'High' or 'Low' only for the α wave and the β wave of the EEG, it was confirmed that it was detectable. Also, training was not required, and detection of exercise intention was realized in a short time of about 1 hour or less.
・We developed an ankle rehabilitation device using a pneumatic cylinder and designed a control system that switches from position (angle) control to force control according to the degree of joint stiffness (hardness) due to contracture. It was confirmed that the target motion could be realized by the control experiment.
・Using an exercise intention detected by L - FTM as a trigger signal, the ankle rehabilitation equipment operated, and it was possible to confirm the part related to exercise and where the brain wave is activated.
In the future, we would like to study methods to improve accuracy and demonstrate experiments to people with actual cerebral infarction.
Acknowledgements
This work was supported partially by MEXT(The Ministry of Education, Culture,Sports,Science and Technology)-Supported Program for the Strategic Research Foundation at Private Universities, 2014-2018(Grant No. S1411038).
Reference
3. K. Kawahira, M. Shimodozono, S. Ogata, N. Tanaka: “Addition of intensive repetition of facilitation exercise to mulutidisciplinary rehabilitation promotes motor functional recovery of the hemiplegic lower limb” Journal of Rehabilitation Medicine 2004; 36: 159-164
4. J Mattout et al, Improving BCI performance through co-adaptation: Applications to the P300-speller, Annals of Physical and Rehabilitation Medicine, Vol. 58, No. 12, pp. 23-28, 2015
5. Andrew J. McDaid, Song Xing, Sheng Q. Xie, Brain Controlled Robotic Exoskeleton for Neurorehabilitation, Proceedings 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp.1039 - 1044 (2013), Pages: 1039 – 1044
6. Y. Joen, C. S. Nam, Y. Kim, M. C. Whang: “Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain-computer interfaces”, International Journal of Industrial Ergonomics, Vol.41, No.5(2011), pp.428-436
7. T. Oda, S. N. Kudoh: “Identification of multiple-tasks-induced-EEG by heuristic BCI with learning type Fuzzy-Template-Matching method”, Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems(IFSA-SCIS), 2017 Joint 17th World Congress of International
8. M. Yachida, H. Wu and Q. Chen, "Face Detection From Color Images Using a Fuzzy Pattern Matching Method", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no.6, pp. 557-563, 1999, 1 September 2016
9. Ying Li, Xiang-lin Qi,Yun-jiu Wang, Eye detection by using fuzzy template matching and feature-parameter-based judgement, Pattern Recognition Letters, Volume 22, Issue 10, Pages 1111-1124,2001.
10. T. Fukuda, S. Ito, F. Arai, Y. Yokoyama, Y. Abe, K. Tanaka, Y. Tanaka, Navigation system based on ceiling landmark recognition for autonomous mobile robot-landmark detection based on fuzzy template matching, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, Vol.2, pp.150-155(1995)
11. T. Oda, S. N. Kudoh: “Identification of multiple-tasks-induced-EEG by heuristic BCI with learning type Fuzzy-Template-Matching method”, Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems(IFSA-SCIS), 2017 Joint 17th World Congress of International
12. N. Saga, N. Saito, "Rehabilitation instrument for prevent contracture of ankle using the pneumatic balloon actuator",Proc. of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,pp.4294-4297(2008.08)
13. K. Yonemoto: “Motion range display and measurement method”, Journal of Japanese Rehabilitation Medicine, Vol. 32, No.4(1995), pp.207-217
14. “NITE Human Characteristics Database”, National institute of technology and evaluation