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

Time–domain feature representation for imagined grip force movement-related cortical potentials(MRCP)of the right or left hand and the decoding of imagined grip force parameters based on electroencephalogram(EEG)activity recorded during a single trial were here investigated.EEG signals were acquired from eleven healthy subjects during four different imagined tasks performed 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 superposition and averaging technology,a single-trial extraction method,analysis of variance(ANOVA),and multiple comparisons.Significantly different features were observed among different imagined grip force tasks.These differences were used to decode imagined grip force parameters using Fisher linear discrimination 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 comparisons 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 4different imagined grip force tasks based on MMP was performed using k-FLDA and SVM,and the average misclassification rates of 27%±5%and 24%±4%across 11 subjects were achieved respectively.The minimum misclassification rate was 15%,and the average minimum misclassification rate across 11 subjects was24%±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.

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.