Wireless Brain-Robot Interface: User Perception and Performance Assessment of Spinal Cord Injury Patients

Abstract

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance.

Greek Traditional Dances: A Way to Support Intellectual, Psychological, and Motor Functions in Senior Citizens at Risk of Neurodegeneration

“…Additionally, the between-group differences were explored comparing score differences between the two groups using either independent samples t -test or Mann–Whitney U -test based on normality assumption of score differences. The methodology used has been published elsewhere (Cramer, 1998; Cramer and Howitt, 2004; Doane and Seward, 2011; Arvanitidou-Vagiona and Xaidits, 2013; Athanasiou et al, 2017; Pandria et al, 2018).…”

Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space

“…There is a study supporting that transfer learning between different subjects by means of source space can achieve higher average single-trial classification accuracy than with a conventional method [ 54 ]. Beyond the BCIC IV 2a dataset that is a common ground for the evaluation of methods decoding multiple MI, we aim to evaluate the improved method on dataset we compiled for the CSI: Brainwave project, containing EEG data of healthy or subjects with spinal cord injury performing multiple motor imagery mainly of the upper limbs [ 36 , 55 ].…”

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

“…The experimental setup has been previously described in detail, including subject assessment [ 39 ] and procedures [ 36 , 37 ], so we will hereby provide only a brief overview. Our experimental protocol was approved by the institutional ethical committee [ 40 ], and all subjects signed an informed consent form.…”

“…The 32 upper extremity motor tasks consisted of 8 independent movements (degrees of freedom) ∗ 2 directions of movement ∗ 2 extremities, comprising the full range of motion of the human arms and were classified into categories for further analysis [ 39 ]. In short, the 8 categories of motor tasks were “Hands,” “Left,” “Right,” “Proximal,” “Distal,” “Rotational,” “Linear,” and “Walking.”…”

“…In our previous work, we have presented our study with a cohort of SCI patients and healthy control subjects that exercised motor imagery to achieve control of anthropomorphic robotic arms in various movement tasks. We have accounted for development [ 36 ], pilot experiments, and brain network analysis [ 37 , 38 ], and we have presented a detailed user perception and performance assessment study, based on neurological and psychometric evaluation [ 39 ]. In the current paper, we present an elaborate analysis of the functional connectivity networks formed on the sensorimotor cortex during visual motor imagery of multiple motor tasks performed by subjects with SCI and healthy controls.…”