Single-trial decoding of imagined grip force parameters involving the right or left hand based on movement-related cortical potentials

• Published in: Chinese science bulletin Vol. 59; no. 16; pp. 1907 - 1916
• Main Authors: Fu, Yunfa, Xu, Baolei, Li, Yongcheng, Wang, Yuechao, Yu, Zhengtao, Li, Hongyi
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer-Verlag 01.06.2014; Science China Press
• Subjects: analysis of variance; brain; Chemistry/Food Science; Earth Sciences; electroencephalography; Engineering; Humanities and Social Sciences; Life Sciences; multidisciplinary; Physics; planning; Science; Science (multidisciplinary); 力参数; 右手; 左手; 支持向量机; 电位; 皮层; 解码; 运动; Brain–computer interfaces (BCIs); Movement-related cortical potentials (MRCP); Electroencephalogram (EEG); Imagined grip force parameters; Single-trial decoding
• ISSN: 1001-6538; 1861-9541
• DOI: 10.1007/s11434-014-0234-5

Time-domain feature representation for imag-ined grip force movement-related cortical potentials （MRCP） of the right or left hand and the decoding of imagined grip force parameters based on electroencepha-logram （EEG） activity recorded during a single trial were here investigated. EEG signals were acquired from eleven healthy subjects during four different imagined tasks per-formed with the right or left hand. Subjects were instructed to execute imagined grip movement at two different levels of force. Each task was executed 60 times in random order. The imagined grip force MRCP of the right or left hand was analyzed by superposifion and averaging technology, a single-trial extraction method, analysis of variance （ANOVA）, and multiple comparisons. Significantly dif- ferent features were observed among different imagined grip force tasks. These differences were used to decode imagined grip force parameters using Fisher linear dis-crimination analysis based on kernel function （k-FLDA） and support vector machine （SVM）. Under the proposed experimental paradigm, the study showed that MRCP may characterize the dynamic processing that takes place in the brain during the planning, execution, and precision of a given imagined grip force task. This means that features related to MRCP can be used to decode imagined grip force parameters based on EEG. ANOVA and multiple com-parisons of time-domain features for MRCP showed that movement-monitoring potentials （MMP） and specific interval （0-150 ms） average potentials to be significantly different among 4 different imagined grip force tasks. The minimum peak negativity differed significantly between high and low amplitude grip force. Identification of the 4 different imagined grip force tasks based on MMP was performed using k-FLDA and SVM, and the average misclassification rates of 27 % 4-5 % and 24 % 4-4 % across 11 subjects were achieved respectively. The mini-mum misclassification rate was 15 %, and the average minimum misclassificafion rate across 11 subjects was 24 % 4-4.5 %. This investigation indicates that imagined grip force MRCP may encode imagined grip force parameters. Single-trial decoding of imagined grip force parameters based on MRCP may be feasible. The study may provide some additional and fine control instructions for brain-computer interfaces.