Interaction of BCI with the underlying neurological conditions in patients: pros and cons

Abstract

The primary purpose of clinical Brain Computer Interface (BCI) systems is to help patients communicate with their environment or to aid in their recovery. BCI can be used to replace, restore, enhance, supplement, or improve natural Central Neural System (CNS) output (Wolpaw and Wolpaw, 2012). A common denominator for all BCI patient groups is that they suffer from a neurological deficit. As a consequence, BCI systems in clinical and research settings operate with control sig-nals (brain waves) that could be substantially altered compared to brain waves of able-bodied individuals. Most BCI systems are built and tested on able-bodied individuals, being insufficiently robust for clinical applications. The main reason for this is a lack of systematic analysis on how different neurological problems affect the BCI performance. This special issue highlights interaction of BCI systems with the underlying neurological problems and how performance of these BCI system differ compared to similar systems tested on healthy individuals. The issue presents 4 reviews (Friedrich et al., 2014; Pineda et al., 2014; Priftis, 2014; Rupp, 2014) and 8 exper-imental studies (Ang et al., 2014; Daly et al., 2014; Ono et al., 2014; Song et al., 2014; Xu et al., 2014; Young et al., 2014a,b,c). It covers studies on five different patient groups: stroke (Ang et al., 2014; Ono et al., 2014; Song et al., 2014; Young et al., 2014a,b,c), spinal cord injury (SCI) (Rupp, 2014; Xu et al., 2014), autism (Friedrich et al., 2014; Pineda et al., 2014), cerebral palsy (CP) (Daly et al., 2014) and amyotrophic lateral sclerosis (ALS) (Priftis, 2014). Three different types of BCI are presented: motor imagery, P300 and neurofeedback (operant conditioning). In the presented papers, BCI has been used either on its own or in a combination with an external device such as a robot or a functional electrical stimulation (FES). Review papers discuss several possible applications of BCI including methods to replace (Priftis, 2014; Rupp, 2014), restore (Rupp, 2014) and improve (Friedrich et al., 2014; Pineda et al., 2014; Rupp, 2014) natural CNP output. Several experimental studies in this special issue present BCI applications to improve and restore CNP functions (Ang et al., 2014; Ono et al., 2014; Young et al., 2014a,b) while some present basic research papers looking into the effect of BCI training on the cortical activ-ity (Song et al., 2014; Young et al., 2014b,c) or exploring EEG signature characteristic for a certain patient group, such as SCI or CP (Daly et al., 2014; Xu et al., 2014). In two review articles Pineda et al. and Friedrich et al. look into the application of BCI on a relatively novel group of patients, autistic children, who show deficits in social and communica-tive skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. They discuss evidences for model-based neurofeedback approach for treating autism and propose a BCI game for treating both high and low functioning autistic patients. The game is unique in that it includes social interactions and provides neural-and body-based feedback that corresponds directly to the underly-ing significance of the trained signals as well as to the behavior that is reinforced. A review by Rupp provides a comprehen-sive critical analysis of pros-and cons-of different types of BCI for spinal cord injured patients. He also discusses advantages and disadvantages of using BCI for communication, wheelchair and environmental control, control of neuroprosthesis and for clinical, rehabilitation purposes. This paper provides a valuable analysis of different medical and personal factors which might affect the performance of a BCI. While some of these factors are specific for spinal cord injured patients, many of them would exist in most patient groups using BCI. A review paper by Priftis provides a critical analysis of the evidences of the effectiveness of P300 speller as a communication tool for ALS patients. This is one of the rare application for which a commercially avail-able solution exists (intendix, g.tec medical engineering GmbH, g.USBamp P300 model, Guger Technologies OG, Austria). While accuracy of this type of BCI reaches 90% in able-bodied, only 70% can be achieved in patients (Ortner et al., 2011). Priftis (2014) therefore concluded that requirements of ALS patients haven't been met yet, and highlights a striking fact that a tiny portion of published papers on P300 BCI presents experimental studies on ALS patients. Papers showing experimental results in the special issue are either oriented toward rehabilitation or toward a basic science research. Stroke remains the most frequently tested patient pop-ulation. In a randomized controlled trial on 21 chronic stroke patients, Ang et al. compare three hand and arm rehabilitation therapies, BCI with a haptic knob (HK) robot, HK alone or a