| 摘要(英) |
This thesis designed and implemented a virtual reality brain computer intarface system about EEG recording and motor imagery based VR character controlling. It is used to solve the limitation in operation space of the existing VR device, and also to help the disabled to use VR with their brain.
In terms of hardware, to improve the convenience and comfort of wearing a EEG device, this thesis combined wireless EEG recorder and VIVE PRO virtual reality head-mounted display. In terms of algorithms, this thesis proposes an innovative normalization method, which can effectively reduce the impact of EEG’s over time behavior changes on the traditional CSP classification accuracy. This thesis also proposed an improved Filter-Bank filtering method, in this way the CSP method can contain the EEG changes with wider bandwidth, combined with this two methods, the CSP achieved 73.7% classification accuracy in the BCI Competition IV dataset 2a with 9 subjects, and an average of 69.9% accuracy is achieved in the data of the 9 subjects recorded by the proposed BCI system. It is 5.9% higher than the traditional CSP method, which greatly improves the usability of the brain computer interface. |
| 參考文獻 |
[1] Siavash Sakhavi; Cuntai Guan; Shuicheng Yan,”Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks”, IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5619-5629, Nov. 2018.
[2] Jaime Camacho; Vidya Manian, “Real-time single channel EEG motor imagery based Brain Computer Interface”, World Automation Congress, Aug. 2016
[3] Tianyou Yu; Jun Xiao; Fangyi Wang; Rui Zhang; Zhenghui Gu; Andrzej Cichocki; Yuanqing Li, “Enhanced Motor Imagery Training Using a Hybrid BCI With Feedback”, IEEE Trans. Biomedical Engineering, vol. 62, no. 7, pp. 1706-1717, Jul. 2015.
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[5] Aimin Jiang; Qing Wang; Jing Shang; Xiaofeng Liu, “Sparse Common Spatial Pattern for EEG Channel Reduction in Brain-Computer Interfaces”, IEEE 23rd Int. Conf. Digital Signal Processing, Feb. 2018.
[6] Haiping Lu; How-Lung Eng; Cuntai Guan; Konstantinos N. Plataniotis; Anastasios N. Venetsanopoulos, “Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting”, IEEE Trans. Biomedical Engineering, vol. 57, no. 12, pp. 2936-2946, Dec. 2010
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[8] Ping Wang; Aimin Jiang; Xiaofeng Liu; Jing Shang; Li Zhang, “LSTM-Based EEG Classification in Motor Imagery Tasks”, IEEE Trans. Neural Systems and Rehabilitation Engineering, vol. 26, no. 11, pp. 2086-2095, Nov. 2018
[9] H. Ramoser; J. Muller-Gerking; G. Pfurtscheller, “Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement”, IEEE Trans. Rehabilitation Engineering, vol. 8, no. 4, pp. 441-446, Dec. 2000.
[10] Sang-Hoon Park; Sang-Goog Lee, “Small Sample Setting and Frequency Band Selection Problem Solving Using Subband Regularized Common Spatial Pattern”, IEEE Sensors Journal, vol. 17, np. 10, pp. 2977-2983, May. 2017.
[11] S. Bermudez i Badia; A. Garcia Morgade; H. Samaha; P. F. M. J. Verschure, “Using a Hybrid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training”, IEEE Trans. Neural Systems and Rehabilitation Engineering, vol. 21, no. 2, pp. 174-181, Mar. 2013.
[12] Amirhossein S. Aghaei; Mohammad Shahin Mahanta; Konstantinos N. Plataniotis, “Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems”, IEEE Trans. Biomedical Engineering, vol. 63, no. 1, pp. 15-29, Jan. 2016.
[13] Shuang Qiu; Weibo Yi; Jiapeng Xu; Hongzhi Qi; Jingang Du; Chunfang Wang; Feng He; Dong Ming, “Event-Related Beta EEG Changes During Active, Passive Movement and Functional Electrical Stimulation of the Lower Limb”, IEEE Trans. Neural Systems and Rehabilitation Engineering, vol. 24, np. 2, pp. 283-290, Feb. 2016.
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