Spatial filters selection towards a rehabilitation BCI

Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step. Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training. Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context

The Promotoer: a successful story of translational research in BCI for motor rehabilitation

Several groups have recently demonstrated in the context of randomized controlled trials (RCTs) how sensorimotor Brain-Computer Interface (BCI) systems can be beneficial for post-stroke motor recovery. Following a successful RCT, at Fondazione Santa Lucia (FSL) a further translational effort was made with the implementation of the Promotœr, an all in-one BCIsupported MI training station. Up to now, 25 patients underwent training with the Promotɶr during their admission for rehabilitation purposes (in add-on to standard therapy). Two illustrative cases are presented. Though currently limited to FSL, the Promotɶr represents a successful story of translational research in BCI for stroke rehabilitation. Results are promising both in terms of feasibility of a BCI training in the context of a real rehabilitation program and in terms of clinical and neurophysiological benefits observed in the patients

GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI

GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training). In this context, GUIDER aims to combine physiological and machine learning approaches. Indeed, GUIDER allows therapists to set parameters and constraints according to the rehabilitation principles (e.g. affected hemisphere, sensorimotor relevant frequencies) and foresees an automatic method to select the features among the defined subset. As a proof of concept, we compared offline performances between manual, just based on operator’s expertise and experience, and GUIDER semiautomatic features selection on BCI data collected from stroke patients during BCI-supported motor imagery training. Preliminary results suggest that this semiautomatic approach could be successfully applied to support the human selection reducing operator dependent variability in view of future multi-centric clinical trials

EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation

The Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients

Electroencephalography (EEG)-Derived Markers to Measure Components of Attention Processing

Although extensively studied for decades, attention system remains an interesting challenge in neuroscience field. The Attention Network Task (ANT) has been developed to provide a measure of the efficiency for the three attention components identified in the Posner’s theoretical model: alerting, orienting and executive control. Here we propose a study on 15 healthy subjects who performed the ANT. We combined advanced methods for connectivity estimation on electroencephalographic (EEG) signals and graph theory with the aim to identify neuro-physiological indices describing the most important features of the three networks correlated with behavioral performances. Our results provided a set of band-specific connectivity indices able to follow the behavioral task performances among subjects for each attention component as defined in the ANT paradigm. Extracted EEG-based indices could be employed in future clinical applications to support the behavioral assessment or to evaluate the influence of specific attention deficits on Brain Computer Interface (BCI) performance and/or the effects of BCI training in cognitive rehabilitation applications

Evaluation of cervical posture improvement of children with cerebral palsy after physical therapy based on head movements and serious games

Background: This paper presents the preliminary results of a novel rehabilitation therapy for cervical and trunk control of children with cerebral palsy (CP) based on serious videogames and physical exercise. Materials: The therapy is based on the use of the ENLAZA Interface, a head mouse based on inertial technology that will be used to control a set of serious videogames with movements of the head. Methods: Ten users with CP participated in the study. Whereas the control group (n=5) followed traditional therapies, the experimental group (n=5) complemented these therapies with a series of ten sessions of gaming with ENLAZA to exercise cervical flexion-extensions, rotations and inclinations in a controlled, engaging environment. Results: The ten work sessions yielded improvements in head and trunk control that were higher in the experimental group for Visual Analogue Scale, Goal Attainment Scaling and Trunk Control Measurement Scale (TCMS). Significant differences (27% vs. 2% of percentage improvement) were found between the experimental and control groups for TCMS (p<0.05). The kinematic assessment shows that there were some improvements in the active and the passive range of motion. However, no significant differences were found pre- and post-intervention. Conclusions:Physical therapy that combines serious games with traditional rehabilitation could allow children with CP to achieve larger function improvements in the trunk and cervical regions. However, given the limited scope of this trial (n=10) additional studies are needed to corroborate this hypothesis

Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing

[No abstract available

Causality estimates among brain cortical areas by Partial Directed Coherence: simulations and application to real data

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. Among various methods established during the years, the Partial Directed Coherence (PDC) is a frequency-domain approach to this problem, based on a multivariate autoregressive modeling of time series and on the concept of Granger causality. In this paper we propose the use of the PDC method on cortical signals estimated from high resolution EEG recordings, a non invasive method which exhibits a higher spatial resolution than conventional cerebral electromagnetic measures. The principle contributions of this work are the results of a simulation study, testing the performances of PDC, and a statistical analysis (via the ANOVA, analysis of variance) of the influence of different levels of Signal to Noise Ratio and temporal length, as they have been systematically imposed on simulated signals. An application to high resolution EEG recordings during a foot movement is also presented

Modern Electrophysiological Methods for Brain-Computer Interfaces

Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI) still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far