Visual Versus Kinesthetic Motor Imagery for BCI Control of Robotic Arms (Mercury 2.0)

Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made. The choice of MI approach could affect individual performance during BCI control, especially for off-the-shelf BCI systems, where ease of use and fast reliable results is the target. Whether for healthy individuals or clinical applications, if such systems are expected to reach consumer maturity, best practices for their use should be investigated. We designed a study to compare VMI and KMI as control modalities of an off-the-shelf EEG-BCI system. 30 healthy individuals (18 male, 12 female) participated in the study, operating two house-developed robotic arms (Mercury 2.0) using an Emotiv EPOC EEG-BCI. They were asked to use first VMI and then KMI to achieve BCI control and we compared the training and success rates. In our study, KMI achieved higher skill percentages during imagery training but VMI achieved higher success rates during BCI control of both robotic arms. Nonetheless, observed differences did not exceed significance thresholds. Individual differences could play a major role in MI performance and should be taken into account when choosing which modality to train for the use of a BCI system.

[1]  Kazuyuki Kanosue,et al.  Task-dependent engagements of the primary visual cortex during kinesthetic and visual motor imagery , 2017, Neuroscience Letters.

[2]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[3]  C. Richards,et al.  Brain activity during visual versus kinesthetic imagery: An fMRI study , 2009, Human brain mapping.

[4]  Panagiotis D. Bamidis,et al.  Comparing Sensorimotor Cortex Activation during Actual and Imaginary Movement , 2010 .

[5]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[6]  Alkinoos Athanasiou,et al.  Development and User Assessment of a Body-Machine Interface for a Hybrid-Controlled 6-Degree of Freedom Robotic Arm (MERCURY) , 2014 .

[7]  Dana Kulic,et al.  Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots , 2009, Int. J. Soc. Robotics.

[8]  Julien Doyon,et al.  The comparison between motor imagery and verbal rehearsal on the learning of sequential movements , 2013, Front. Hum. Neurosci..

[9]  Panagiotis D. Bamidis,et al.  Density based clustering on indoor kinect location tracking: A new way to exploit active and healthy aging living lab datasets , 2015, 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE).

[10]  Muhammad Abd-El-Barr,et al.  Long-term Training With a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. , 2016, Neurosurgery.

[11]  A. Elhan,et al.  Investigation of Four Different Normality Tests in Terms of Type 1 Error Rate and Power under Different Distributions , 2006 .

[12]  Helen E. Savaki,et al.  Observation of action: grasping with the mind's hand , 2004, NeuroImage.

[13]  P. Jackson,et al.  The neural network of motor imagery: An ALE meta-analysis , 2013, Neuroscience & Biobehavioral Reviews.

[14]  Bin He,et al.  Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks , 2016, Scientific Reports.

[15]  L. Xu,et al.  Motor execution and motor imagery: A comparison of functional connectivity patterns based on graph theory , 2014, Neuroscience.

[16]  M. Lotze,et al.  Motor imagery , 2006, Journal of Physiology-Paris.

[17]  Dean J Krusienski,et al.  Brain-computer interfaces in medicine. , 2012, Mayo Clinic proceedings.

[18]  Mahyar Hamedi,et al.  Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review , 2016, Neural Computation.

[19]  Alkinoos Athanasiou,et al.  Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes , 2013, Adv. Hum. Comput. Interact..

[20]  Panagiotis D. Bamidis,et al.  Thessaloniki Active and Healthy Ageing Living Lab: the roadmap from a specific project to a living lab towards openness , 2016, PETRA.

[21]  Panagiotis D. Bamidis,et al.  Development of MERCURY version 2.0 robotic arms for rehabilitation applications , 2015, PETRA.