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

• Published in: Journal of neuroengineering and rehabilitation Vol. 14; no. 1; pp. 93 - 10
• Main Authors: Wang, Kun, Wang, Zhongpeng, Guo, Yi, He, Feng, Qi, Hongzhi, Xu, Minpeng, Ming, Dong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 11.09.2017; BioMed Central Ltd; BMC
• Subjects: Adult; Algorithms; Biomechanical Phenomena; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Brain; Brain-computer Interface (BCI); Brain-Computer Interfaces; Commands; Computer applications; Contraction; Disabilities; EEG; Electroencephalogram (EEG); Electroencephalography; Electroencephalography Phase Synchronization; Electromyography; Emulation; Energy Metabolism - physiology; Event-related Desynchronization (ERD); Evoked Potentials; Experiments; Feasibility Studies; Feedback; Female; Force load; Hand - physiology; Healthy Volunteers; Human-computer interaction; Human-computer interface; Humans; Imagery; Imagination - physiology; Implants; Interfaces; Internet; Kinematics; Load; Loads (forces); Male; Mental task performance; Methods; Motor imagery; Movement - physiology; Muscle Contraction - physiology; Neurology; Neurosciences; Online Systems; Oscillations; Perturbation methods; Rehabilitation; Rehabilitation Medicine; Skeletal muscle; Spatial distribution; Stroke; Support Vector Machine; Support vector machines; Synchronization; Technology application; User training; Young Adult; Motor imagery; Event-related Desynchronization (ERD); Electroencephalogram (EEG); Brain-computer Interface (BCI); Force load
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/s12984-017-0307-1

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.