A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study

Abstract

Background: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.

Methods: Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.

Results: All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution.

Conclusions: This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.

Keywords: Brain-computer Interface (BCI); Electroencephalogram (EEG); Event-related Desynchronization (ERD); Force load; Motor imagery.

Fig. 1

The timeline of one trial of the experimental paradigm. Sessions 1, 2 and 3 have two tasks, high force load and low force load. Each trial has a motor execution task followed by a motor imagery task at the same force load. There were two blocks during these sessions and each block included 10 trials (5 trials of high and 5 trials of low, randomly sorted). Session 4 has only one task, relaxed (30 trials). Session 5 has three tasks (high, low and relaxed). It consisted of four blocks, each of which included 12 trials (4 trials for high, 4 trials for low and 4 trials for relaxed, randomly sorted). Sessions 2, 3 and 5 present a voice feedback after each motor imagery task to indicate whether the classifier correctly identified the imagery force load level

Fig. 2

Examples of the EMG signals for one representative subject and the comparison of IEMG values. a The EMG signals of one subject during seven experimental tasks. b The comparison of IEMG values between RX task and other tasks. MEH and MEL show significant differences compared to relaxed tasks, while other tasks, motor imagery tasks, show minimal differences

Fig. 3

Classification performance. a Classification accuracies for each subject during the experiment (each color represented one subject). Session 1 to 3 identify two classifications, ‘high’ versus ‘low’, while session 5 identifies three classifications, ‘high’ versus ‘low’ versus ‘relaxed’. b Online output distribution in session 5

Fig. 4

Time-frequency analysis of EEG for different mental tasks. a The averaged time-frequency maps on C3 under multiple force load motor imagery tasks. The vertical line was marked at the onset of task, the blue color indicates the ERD phenomenon. b The mean ERSP on C3 at mu and beta rhythms during two stages of the experiment. c The averaged ERSP topography under multiple force load motor imagery tasks. d The significant difference topography between the high force load MI and low force load MI