Brain–computer interface targeting non-motor functions after spinal cord injury: a case report

Abstract

Study design: This is a case report. Objectives: The objective of this study was to report on a brain-computer interface (BCI) paradigm that is successfully used with an inpatient spinal cord injury patient. Setting: This study was conducted in an inpatient rehabilitation hospital. Methods: A 25-year-old man with a C5 burst fracture and subsequent tetraplegia (The American Spinal Injury Association) participated in this case study. He completed a brief battery of psychological, pain, cognitive and other screening measures at points before and after the BCI paradigm during his rehabilitation hospitalization. Results: The paradigm was easily learned and well tolerated with no adverse effects. Conclusions: This case is reflective of the trends in our ongoing feasibility study evaluating BCI technology in the inpatient rehabilitation setting. Clinical implications and challenges of using this technology in a busy hospital unit are reviewed. Spinal Cord (2015) 53, S25-S26; doi:10.1038/sc.2014.230 INTRODUCTIONIn an attempt to widen the utility of computer-assisted devices and to increase independence among spinal cord injury (SCI) populations, brain-computer interface (BCI) technology has generated great interest. 1 BCIs do not depend on the central nervous systems normal output pathways that have been compromised following injury. Instead, participants overtly manipulate their brain activity as compared with using motor movements to produce signals that can be used to control devices and/or virtual environments. 2 To date, the majority of BCI studies in persons with SCI have revolved around motor-based activity and communication with case examples and small studies.The potential of BCI in neuropsychological facets of SCI recovery is our group's focus. Specifically, we foresee BCI technology as a possible tool for pain and stress management, leisure activity exploration and cognitive assessment/ intervention for patients with more severe motor impairment such as tetraplegia. This interest is further fueled by increasing support for psychological, social and non-motor-based factors as key aspects in perceived quality of life following SCI. 3 Introduction of BCI during inpatient rehabilitation allows for extensive training and modification of the technology, which would be less feasible on an outpatient basis.To our knowledge, BCI technology has not been used with inpatient SCI patients in a systematic manner as part of inpatient rehabilitation treatment. This case study is a part of our larger feasibility study including standard screens of mood, pain and cognition on our inpatient unit to evaluate whether BCI technology is tolerated, enjoyed and could be used in future work targeting previously mentioned therapeutic goals.

Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation

“…Results showed an increase of such indexes that are associated with logical thinking, problem solving, and attentiveness to external stimuli, suggesting an effect of the performed training also on nonmotor functions. Salisbury and colleagues [ 48 ] presented a single-case feasibility study on a patient with spinal cord injury (SCI), where EEG-BCI was employed for reducing pain and improving nonmotor functions such as mood and cognition, as part of inpatient rehabilitation treatment. Even if no data are presented about the described therapeutic goals, the study supported the feasibility and tolerability of this approach in the patient.…”

Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review

“…for any invertible X, and the self-duality 11 The geometric mean of two positive matrices C1 and C2 has been studied since the 1970's by electrical engineers, physicists and matrix analysts. A closed-form expression for it is known.…”

“…They concluded that this choice should be preferred for N small, which is in line with our theoretical analysis. Reference [11] proposed a simple data augmentation approach for improving the performance of the Riemannian MDM algorithm. A hierarchical MDM classifier for multi-class problem has been tested in [117].…”

“…The study of the applicability of BCI technology to clinical populations (e.g., [6][7][8][9][10][11][12][13][14]), for instance, in comparison (or combination) to other Assistive Technology (AT) such as eyetracking, electrooculography and contactors [15,16].…”

Sequential Detection of P300 Waves for High-Throughput Brain-Computer Interfaces

“…The P300 wave has been widely used for brain-computer interfaces (BCIs) [3,4]. For example, the P300 speller [5] is designed to infer a user's intention solely from brain signals and is a valuable tool for individuals with physical disabilities [6][7][8]. A user focuses on a character among 36 candidates arranged in a 6 × 6 matrix (Figure 1(a)).…”