A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study
Wang et al. Journal of NeuroEngineering and Rehabilitation
A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study
Kun Wang 0 1 2
Zhongpeng Wang 0 1 2
Yi Guo 1 2
Feng He 1 2
Hongzhi Qi 1 2
Minpeng Xu 1 2
Dong Ming 1 2
0 Equal contributors
1 Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University , Tianjin , China
2 Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University , Tianjin , China
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.
Force load; Motor imagery; Electroencephalogram (EEG); Event-related Desynchronization (ERD); Braincomputer Interface (BCI)
Background
In the past several decades, an increasing number of
researchers have focused on decoding information from brain
which could be applied to construct brain-computer
interfaces (BCIs). BCIs can provide a direct communication
pathway between the brain and external devices without
using peripheral nerves and muscles [
1
]. Motor
imagerybased BCI (MI-BCI) is one of the most important BCI
paradigms. An outstanding advantage of the paradigm is that
it requires no real action from users. It has been
demonstrated that motor imagery could induce the event-related
de-synchronization/synchronization (ERD/ERS)
phenomena occuring at 8-13 Hz (mu rhythm) and 14-30 Hz (beta
rhythm) [
2
], which could be reliably recognized through
appropriate algorithms, such as, power spectral density,
source imaging method and so on.
Several clinical applications of MI-BCI systems have
been reported. MI-BCI not only can be used as a
communication or control methods to help patients with serious
movement disorder like ALS [
3
], cerebral palsy [
4
], etc. to
complete the daily interactions. More importantly,
MIBCI is more and more used in the recovery of stroke.
Several studies have confirmed that MI-BCI is an effective
method for post stroke rehabilitation [
5–7
]. In these
studies, a variety of functional devices, such as functional
electrical stimulation (FES) [
8
], rehabilitation robots [
9
], etc.,
were used in combination with MI-BCI to construct a
close loop neurofeedback from the sensorimotor cortex to
paralyzed limbs [
8–11
].
Novel motor imagery paradigms have been designed to
decode motion intention accurately and to improve
MIBCI control performance efficiently. Compound and
sequential limb motor imagery have been proved to be
divisible as well as the simple limb [
12, 13
]. The
combination of simple and compound limb motor imagery enabled
the MI-BCI to control a quadcopter with
threedimensional movements [14]. In recent years, many
researchers made attempt to decode the fine motion
intentions on the single limb, aiming at building the connection
between the MI task and the corresponding action of the
output device. Edelman et al. have classified four different
postures MI tasks of the right (...truncated)