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

Abstract

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 4 different 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 was 24 % ± 4.5 %.

Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis

“…Temporal features presented here were obtained from the pre-movement section of the MRCP signals which contains information on movement preparation and characteristics of the users' mental state. This is supported by previous reports that show that the MRCP can reveal movement execution parameters such as force and speed [41,42] or indeed attention variations [20,21]. In the current study, spectral features were also obtained from five frequency band powers.…”

Subthalamic nucleus beta and gamma activity is modulated depending on the level of imagined grip force

“…Yet it seems difficult to discriminate between different imagined force levels from cortical recordings alone ( Murphy et al, 2016 , Zaepffel et al, 2013 ). To our knowledge only one study has successfully extracted features that carry information about imagined force from scalp EEG ( Fu et al, 2014 ). Feature extraction was based only on low-pass filtered data and the contribution of beta or gamma activity was not examined.…”

Auditory attention detection in cocktail-party: A microstate study

“…Different parameters of EEG microstates explain the characteristics of neural activity (Fu et al, 2014; Khanna et al, 2015). Three temporal dynamics parameters are used in this study, including coverage, duration, and occurrence of each microstate map.…”