Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface.

OBJECTIVE: We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed. METHODS: We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping. RESULTS: A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively. CONCLUSIONS: Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs. SIGNIFICANCE: Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed.

Selection of the method to appraise and compare health systems using risk stratification: the ASSEHS approach.

To face the challenge of active and healthy ageing, European Health Systems and services should move towards proactive, anticipatory and integrated care. The comparison of methods to combine results across studies and to determine an overall effect was undertaken by the EU project ASSEHS (Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services, EU project (No. 2013 12 04). The questions raised in ASSEHS are broad and involve a complex body of literature. Thus, systematic reviews are not appropriate. The most appropriate method appears to be scoping studies. In this paper, an updated method of scoping studies has been used to determine the questions needed to appraise the health systems and services for frailty in the ageing population. Three objectives were set (i) to detect a relevant number of risk stratification tools for frailty and identify the best-in-class, (ii) to understand the feasibility of introducing stratification tools and identify the difficulties of the process and (iii) to find evidence on the impact of risk stratification in Health Services. This novel approach may provide greater clarity about scoping study methodology and help enhance the methodological rigor with which authors undertake and report scoping studies.

A new (semantic) reflexive brain-computer interface: in search for a suitable classifier.

The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.