User-centered design in brain–computer interfaces — a case study

The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found

Testing the significance of connectivity networks: Comparison of different assessing procedures

Despite the well-established use of partial directed coherence (PDC) to estimate interactions between brain signals, the assessment of its statistical significance still remains controversial. Commonly used approaches are based on the generation of empirical distributions of the null case, implying a considerable computational time, which may become a serious limitation in practical applications. Recently, rigorous asymptotic distributions for PDC were proposed. The aim of this work is to compare the performances of the asymptotic statistics with those of an empirical approach, in terms of both accuracy and computational time. Methods: Indices of performance were derived for the two approaches by a simulation study implementing different ground-truth networks under different levels of signal-to-noise ratio and amount of data available for the estimate. The two approaches were then applied to the resting-state EEG data acquired in a group of minimally conscious state and vegetative state/unresponsive wakefulness syndrome patients. Results: The performances of the asymptotic statistics in simulations matched those obtained by the empirical approach, with a considerable reduction of the computational time. Results of the application to real data showed that the asymptotic statistics led to the extraction of connectivity-based indices able to discriminate patients in different disorders of consciousness conditions and to correlate significantly with clinical scales. Such results were similar to those obtained by the empirical assessment, but with a considerable time economy. Significance: Asymptotic statistics provide an approach to the assessment of PDC significance with comparable performances with respect to the previously used empirical approaches but with a substantial advantage in terms of computational time

P300 latency Jitter occurrence in patients with disorders of consciousness: Toward a better design for Brain Computer Interface applications

In this study the P300 latency jitter has been explored in an EEG data set collected from a group of patients with disorders of consciousness (DOC; n=13) that was administered with an auditory Oddball paradigm under passive and active conditions. A method based on wavelet transform was applied to estimate single trial P300 waveforms. Preliminary results showed that 5 Vegetative State (VS) and 8 Minimally Conscious Staten (MCS) patients exhibited significantly higher values of P300 latency jitter as compared to those obtained from a control group of 12 healthy subjects. In addition, the magnitude of the P300 latency jitter negatively correlated with patients' clinical status. The existence of such phenomenon might substantially limit an effective use of Brain Computer Interface systems for communication

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke. © 2018 Toppi, Astolfi, Risetti, Anzolin, Kober, Wood and Mattia

On ERPs detection in disorders of consciousness rehabilitation

Disorders of Consciousness (DOC) like Vegetative State (VS), and Minimally Conscious State (MCS) are clinical conditions characterized by the absence or intermittent behavioral responsiveness. A neurophysiological monitoring of parameters like Event-Related Potentials (ERPs) could be a first step to follow-up the clinical evolution of these patients during their rehabilitation phase. Eleven patients diagnosed as VS (n = 8) and MCS (n = 3) by means of the JFK Coma Recovery Scale Revised (CRS-R) underwent scalp EEG recordings during the delivery of a 3-stimuli auditory oddball paradigm, which included standard, deviant tones and the subject own name (SON) presented as a novel stimulus, administered under passive and active conditions. Four patients who showed a change in their clinical status as detected by means of the CRS-R (i.e., moved from VS to MCS), were subjected to a second EEG recording session. All patients, but one (anoxic etiology), showed ERP components such as mismatch negativity (MMN) and novelty P300 (nP3) under passive condition. When patients were asked to count the novel stimuli (active condition), the nP3 component displayed a significant increase in amplitude (p = 0.009) and a wider topographical distribution with respect to the passive listening, only in MCS. In 2 out of the 4 patients who underwent a second recording session consistently with their transition from VS to MCS, the nP3 component elicited by passive listening of SON stimuli revealed a significant amplitude increment (p < 0.05). Most relevant, the amplitude of the nP3 component in the active condition, acquired in each patient and in all recording sessions, displayed a significant positive correlation with the total scores (p = 0.004) and with the auditory sub-scores (p < 0.00001) of the CRS-R administered before each EEG recording. As such, the present findings corroborate the value of ERPs monitoring in DOC patients to investigate residual unconscious and conscious cognitive function

Vegetative state, minimally conscious state, akinetic mutism and Parkinsonism as a continuum of recovery from disorders of consciousness: an exploratory and preliminary study

The aim of this study was to review the usefulness of clinical and instrumental evaluation in individuals with disorders of consciousness (DOC). Thirteen subjects with severe acquired brain injury (ABI) and a diagnosis of DOC were evaluated using the Coma Recovery Scale in its revised version (CRS-R) and a new global disability index, the Post-Coma Scale (PCS). These instruments were administered both by a neutral examiner (professional) and by a professional in the presence of a caregiver. All patients were also scored using the International Classification of Functioning, Disability and Health (ICF). A statistically significant correlation between CRS-R and PCS was demonstrated. However, there also emerged significant differences in responsiveness between professional versus caregiver+professional assessment using the two scales. The emotional stimulation provided by significant others (caregivers) during administration of DOC evaluation scales may improve the assessment of responsiveness

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke

Brain-computer interfaces for assessment and communication in disorders of consciousness

Many patients with Disorders of Consciousness (DOC) are misdiagnosed for a variety of reasons. These patients typically cannot communicate. Because such patients are not provided with the needed tools, one of their basic human needs remains unsatisfied, leaving them truly locked in to their bodies. This chapter first reviews current methods and problems of diagnoses and assistive technology for communication, supporting the view that advances in both respects are needed for patients with DOC. The authors also discuss possible solutions to these problems and introduce emerging developments based on EEG (Electroencephalography), fMRI (Functional Magnetic Resonance Imaging), and fNIRS (Functional Near-Infrared Spectroscopy) that have been validated with patients and healthy volunteers

The auditory P300-based single-switch brain–computer interface:Paradigm transition from healthy subjects to minimally consciouspatients

Objective: Within this work an auditory P300 brain–computer interface based on tone stream segregation,which allows for binary decisions, was developed and evaluated.Methods and materials: Two tone streams consisting of short beep tones with infrequently appearingdeviant tones at random positions were used as stimuli. This paradigm was evaluated in 10 healthysubjects and applied to 12 patients in a minimally conscious state (MCS) at clinics in Graz, Würzburg,Rome, and Liège. A stepwise linear discriminant analysis classifier with 10 × 10 cross-validation was usedto detect the presence of any P300 and to investigate attentional modulation of the P300 amplitude.Results: The results for healthy subjects were promising and most classification results were better thanrandom. In 8 of the 10 subjects, focused attention on at least one of the tone streams could be detectedon a single-trial basis. By averaging 10 data segments, classification accuracies up to 90.6 % could bereached. However, for MCS patients only a small number of classification results were above chance leveland none of the results were sufficient for communication purposes. Nevertheless, signs of consciousnesswere detected in 9 of the 12 patients, not on a single-trial basis, but after averaging of all correspondingdata segments and computing significant differences. These significant results, however, strongly variedacross sessions and conditions.Conclusion: This work shows the transition of a paradigm from healthy subjects to MCS patients. Promisingresults with healthy subjects are, however, no guarantee of good results with patients. Therefore, moreinvestigations are required before any definite conclusions about the usability of this paradigm for MCSpatients can be drawn. Nevertheless, this paradigm might offer an opportunity to support bedside clinicalassessment of MCS patients and eventually, to provide them with a means of communication